Bioengineered Nanoparticle and Environmental Particulate Matter Toxicity: Mechanisms, Regulations and Applications

Abstract

Bioengineered nanoparticles, and the inorganic fume agglomerates and detritus mineral ores include soft and hard particulates that differ in size distribution, surface properties and metabolites, and in dissolution kinetics. The subtypes of detritus-class microparticulates include the polyhedrally-bonded and ionic mineral- containing, inaddition to the other transition metal -oxide or -silicon oxide forms. Exposure to particle cumuli and any effect modifiers will result in the particulate matter-related disease. The initial observations on exposure-related effects of incompletely combusted products, while the remainder of earlier evidence on the association stems from epidemiologic studies. Both native and combustion composition particulates are associated with pathology, chemically synthesized nanoparticles have been designed for capillary type interstitium-pore selective passive theranostic applicability and high-affinity targeted binding to cell surface proteins with the aim of exterior biocompatibility. In this chapter, the existing knowledge on methodologies for in vitro characterization of particulate matter, systemic biodistribution modeling of pharmacodynamic toxicokinetics and assessment of small molecule chemoxenobiotics efficacy, determination of environmental particulate matter exposure-related causation, standards for air sampling and exposure limits, surveillance monitoring and implementation of bioengineering controls, is covered.

1. Introduction

Particulates span bioengineered nanoparticles (NP) to geologic detri and fume agglomerates, and either soft nanoparticles or hard particulates with distinct dissolution barrier energetics [1, 2] and forms of toxicity [3]. These types of nanoparticles include colloidal such as carbon black and soot [4]. Beryllium and transition metal oxide agglomerates or aggregates [5], or unionized gold or silver colloidal nanoparticles with the oxidized electrophilic surface [6], shell-coated, aminosilane ionic coat on shell surface modification [7], and PEGylated with covalently-bound etheroylated isophilic repeating unit chain to coat [8]. Size distribution is between nanometers [9] to microns; composition, size, aspect and surface properties [10] are associated with particulate matter exposure-related toxicity [8, 11], which includes the nuisance dusts [12]. The initial observations on exposure-related effects of incompletely combusted products begin in 1775 with those of Percivall Pott on the soot composition being carcinogenic in chimney sweeps [13] as the initial cross-sectional study. The remainder of the evidence on association and causality subsequent to is by the application of non-parametric statistical methods, and is from the epidemiologic studies, both case-control with comparative groups and retrospective or prospective cohort [14].

Particulate toxicity can be studied at the single cell level, in experimental small animal subjects and by time-weighted average (TWA) air sampling-coupled to functional assessments of exposed persons, which is by air sampling with filter-threshold devices or flow density separation elutriation [15, 16], and study of nanoparticulate matter particle size distributions adsorbed on grids or of the sub-cellular morphology with electron diffraction imaging (TEM) [17], or by detection of size differences in solution with dynamic-light scattering (DLS) [18], and enhanced dark field microscopy (EDFM) with hyperspectral imaging (HSI) for accurate detection of sampled less dense NPs on filters (i.e. MCE) [19]. Isolation can be also be by specimen digestion and particle fractionation with detection at 10⁻¹² M concentration resolution [20], and nanoparticle properties characterization is coupled to toxicity assessments at single cell molecular scale resolution; and in this day combined to high-sensitivity study of gene expression by quantitative PCR (qPCR), RNA sequencing, and epigenetic changes by bisulfite genomics imaging [21].

The d-orbital block detritus minerals between Group 3 – Group 12 are the transition metals with polyhedral bonding configuration to Group 16 nonmetals, and the subtypes of dichotomous earth particulates include the inosilicates such as Silicon dioxide (SiO₂) or partial oxidation state ideal ores such as Copper ore (Cu₁₂As₄S₁₃) with polyhedral bond configuration crystal lattice structures determinable by X-ray diffraction [22], inaddition to the asbestos classes of ionic minerals-containing rock with silica-based ionic composition; and there are also the other non-crystalline structure oxides such as the synthetic amorphous and biogenic silicates. There are several compositions of ore lithificates, and regional contamination secondary to industrial processes.

Inaddition to the naturally-occurring detritus particulates, there are the combustion-generated particulate oxides that agglomerate over time [23] with increased particle size with lower degradability and increased toxicity risk, and the chemically-synthesized monodisperse Zinc (II)- or Cadmium (II)- based transition metal-nonmetal (Se²⁻, S²⁻) semi-conductor materials that are valence-conduction band gap size-tunable for variable wavelength emission properties with applicability to electronic systems [24]. The hard NPs, ferrous or ferric iron oxides (FeO, Fe₂O₃) have been utilized for supraparamagnetic MRI (T₂W) [25] and cell tracking by transfection loading [26]; whereas the others, soft nanoparticles, with the classic four-electron C-atom bonding arrangement and exterior biocompatibility are within liposomal phospholipid encapsulation and in dendritic forms with diaminobutane cores that are utilized for biomedical application small molecule chemoxenobiotic enhanced permeation and retention (EPR). Toxicities include immediate systemic inflammatory response and hypersensitivity with earlier formulations, immunogenic sensitization with repeated administrations that also applies to nervous tissue treatments [27], while biodistribution to reticuloendothelial cell-containing tissues also limits efficacy [28].

Both hard and soft matter are in the inhalable size range (1–100 μm) [29], and result in risk of direct toxicity through air, water or food, and waste bioaccumulates to environment including plastics. In this chapter, the current day principles on bioengineered NP and environmental particulate matter exposure-related pathology causation, particulates molecular structure and cell biomolecular pathways activation, regulatory standards for exposure limits, industrial hygiene and cell responses to the potential for either immediate or delayed cellular toxicity are presented.

2. Particulates, aerosols and droplets and respiratory tree deposition

Nano-sized particulates (NPs), agglomerated nano- or micro-nanoparticles, can be defined as nanometer (nm) to micron size agglomerates with nanoparticle size in one dimension (1-D) being ≤100 nm, while particulate or agglomerate structural irregularity necessitates characterization by aerodynamic size with adjustment for air flow effects inhomogeneity in real conditions. The inhalable size range of particles, agglomerates and large aerosols includes Zika virus (45 nm, single), smoke (400–700 nm), bacterium (1–5 μm), dust particle (2.5 μm), cells (RBC, 8 μm), pollen (15 μm) and extends into droplets (> 100 μm), Table 1. Detritus minerals and particulates. Fine particles are the 2 nm to 2 μm size range of nanoparticulates including atmospheric aerosols with several different molecules that are found in association include sulfate (SO₄²⁻), carbon (soot), lead, ammonium (NH₄⁺), As, Se and protons (H⁺); and coarse particles constitute the 2 μm to 100 μm interval of nanoparticulates with iron, calcium, titanium, magnesium, potassium, phosphate (PO₄²⁻), silicon, aluminum and organic (i.e. pollen and plant matter). The formation of droplets in tri-modal distributions results from volatile gas to hot vapor and condensation upon cooling to primary particles and chain aggregates (∼5 nm – 100 nm) [51], or gas chemical conversion to low volatility vapor, nucleation and condensation growth of aggregated nuclei into the larger droplet forms with coagulation (50 nm – 8 μm), while the mechanically-generated aerosol range (1–90 μm) is for combination aerosol particulates with emissions, dust, volcano ash and plant matter; with formation, the weighted increase in size results in rainout or sedimentation depending on size range, and the 90 nm to 2 micron range is known as the accumulation range.

Particulate (Size^&1)	Example	Formula / Composition^&2	Gene expression (Inc, no δ)^&3	Gene expression (dec)	Regulation / IARC classification^&4 {IARC, 2012 [30]}	Ref. {Hygienists, 2021 [15]}
Inosilicate (> 5 μm (l.))	Crocidolite	[(Na^+,Fe^2+3 Fe^3+2)Si8O22(OH)2]n	Ceacam1, Espl1, Sult1b1, Sult2b1; Sema3a (A)	Abcc10, Rbck1	TLV-TWA, PEL-TWA, REL-TWA, 0.1 f/mL^!	{Boulanger, 2014; Selikoff, 1978; Pascolo, 2013; Perkins, 2015} [31, 32, 33, 34]
	Chrysotile^!	[(Mg^+3)Si2O5(OH)4]n	—			—
	Amosite (Cummingtonite-Grunerite)^%	[(Mg^2+, Fe^2+)7Si8O22(OH)2]n	Cxcl6 (Il-8), Cxcl6, Ptgs2, Tnf (B)	n/a		{Pascolo, 2013; Duncan, 2014} [33, 35]
	Anthophyllite	[(Mg^2+, Fe^2+)7Si8O22(OH)2]n	—			—
	Ferroactinolite	[Ca^2+2(Mg^2+, Fe^2+)5Si8O22(OH)2]n
	Tremolite	[Ca^2+2(Mg^2+5,Fe^2+)Si8O22(OH)2]n	—			{Webber, 2008; Duncan, 2014} [35, 36]
Silicate (2.16 μm, A - 3.55 μm, C)	Amorphous (Monodisperse)	(Si02,4)n	Txbip (A)	Fst, Rrm2	TLV-TWA, 10 mg/m^3; PEL-TWA, 80 mg/m^3/ (%SiO2)	{Dove, 2008; Albers, 2015; Meijerink, 2019; Perkins, 2012} [1, 2, 37, 38]
	Crystalline –Cristobalite (Disperse)*		Trfc, Cxcr4, Mx2, Col4a6, Tlr1, Cxcl6 (Il-6), Stat5a, Fas, Jun, Mx2, Mmp1, Il-8, Bcl2a1, Tnfaip3 (A)	Ppargc1a, Tp53, Vegfa, Wnt5a, Stat5a, P13k, CD14, Cebpa, Bcl2, Sulf1	TLV-TWA, 0.05 mg/m^3; PEL-TWAQuartz = 10 mg/m^3/ (%SiO2 + 2), Quartz; PEL-TWACristobalite, 0.5 x PEL-TWAQuartz	{Dove, 2008; Vallières, 2016; Wan, 2017; Perkins, 2015; Perkins, 2012; Jiang, 2008; Uboldi, 2016} [2, 34, 38, 39, 40, 41, 42]
Nesosilicates (−)	Spessartine	[3 Mn^2+, 2 Al^3+(SiO4)3]n*	—	—	—	—
Sorosilicates (−)	Axinite	[3 (Ca^2+, Fe^2+, Mn^2+), 2 Al^3+ (BO3)(Si4O12)(OH)]n
Cyclosilicates (−)	Tourmaline	[(Na^+,Ca^2+), 3 (Al^3+, Li^+, Mg^2+), 6 (Al^3+, Fe^2+, Mn^2+) (Si6O18)(BO3)3(OH)4]n
	Beryl	[(3 Be^+, 2 Al^3+)Si6O16]n	—		—	—
Copper ore / Copper NP (1–5, 500 nm)	Cupric, ionic (n/a)	Cu^2+(C2H3O2)2, CuSO4	Hmox1, Nfkb1, Il-8, Thbs1, Relb, Egf, Icam1 (C)	Angpt2, Igfbp3, RelA	TLV-TWA, PEL-TWA, 1 mg/m^3 (dust)	{Meijerink, 2019; McElwee, 2009} [37, 43]
	Tetrahedrite	12 (Cu^2+, Fe^3+/2+) Sb4S13
	Chrysocolia	2 (Cu^2+, Al^3+) H2Si2O5; (OH)4; ·n (H2O)
	Tenorite (or smelter oxide)	CuO	—
	Cuprite	Cu2O
	Chalcopyrite	CuFeS2
	Digenite	Cu9S5
	Enargite	Cu3AsS4	Nfe2l2 (Nrf-2), Hif1a, Cox2^	Mir199a^		{He, 2014; Smith, 1998} [44, 45]
	Tennantite	12 (Cu^+, Ag^+, Zn^2+, Fe^2+ / 3+) As4S13
Nano-Cobalt (20 nm, μ; 260 nm, agglom.)	Cobalt (Colloidal, Oxide)	CoO, Co(OH)3	Cxcl1, Mki67, Pcna (D)	—	TLV-TWA, 0.02 mg/m^3; PEL-TWA, 0.1 mg/m^3	{Wan, 2017; Meijerink, 2019} [37, 40]
Nano-Silver (114–159 nm, 1.48 μm)	Silver (Colloidal, Oxide)	AgCl, Ag2O, AgClO4	Cxcl8 (Il-8), CCL3/−4 (Mip-1α/−1β) (E)	—	PEL-TWA, 0.01 mg/m^3	{Vallières, 2016} [39]
Nano-Titania (12–95 nm, 28 nm (μ); 280 nm (μ, agglom.), 2.48 μm)	Titanium (Oxide)	TiO2	n/c	n/c	—	{Vallières, 2016; Meijerink, 2019; Uboldi, 2016} [37, 39, 42]
Metal oxides (i.e. Lead fume, 12–95 nm, 2.9 μm)	Lead oxide	PbO, Pb3,4O4	Cxcl6, IL1b, Tnfα, Ccl3; Tek, Vegfa, Flt1, Fgf2 (E)	—	PEL-TWA, 50 μg/m^3 =	{Landrigan, 2022; Steenland, 1992; Machoń-Grecka, 2017} [5, 46, 47]
	Zinc oxide	ZnO2	Ccl2 (Mcp-1), Rantes, Tnfα, Cxcl8; Vimentin (protein); (D)	—	PEL-TWA, 5 mg/m^3 (fume), 15 mg/m^3 (total dust); STEL-TWA15 min, 10 mg/m^3; REL, 2 mg/m^3	{Vallières, 2016} [39]
	Oxides mixture (5 nm – 20 μm, 10–30% ≥ 1 μm)	FemOn, CrmOn	—	—	TLVmix^▲	{Park, 2014} [23]
Metal halogen (weld fume, 3–180 nm)*	Zinc (smelt, oxide; ionic)	ZnCl2	—	—	PEL-TWA 1 (STEL) – 2 mg/m^3	{Vallières, 2016} [39]
	Manganese (weld, μ 11–47 nm)	MnF2	Dmt1^#	Slc30a10, Slc39a14, Rbfox1	TLV-TWA, 0.02 mg/m^3 (resp), 0.1 mg/m^3 (inh); REL-TWA 1 mg/m^3; PEL, STEL-TWA, 5 mg/m^3 (dust), 3 mg/m^3	{Bozack, 2021; Lindner, 2022; Balachandran, 2020} [48, 49]
Metal acetate; (−)	Lead, ionic	Pb^2+(C2H3O2)2	Flt1, Kdr, Vegfa, Vegfb (F); Bmp6 (no δ), Fos/Jun (no δ) (G)	NF-κB, Col10a	=	{Machoń-Grecka, 2017; Zuscik, 2002} [47, 50]

Table 1.

Detritus minerals and particulates.

*

, Zn or Mn (2⁺, ox), soluble ion form as most common at pH < 8 (Pourbaix), or 3⁺ oxidation in presence of ROS; #MPP+ responsive; ^Arsenite (AsO₃) exposure-applicable differential gene expression; n/c, no change; A, amorphous; C, Cristobalite; ^&1, ^a) Size, Phase contrast (40-100x, objective), TEM, STEM, XRD, DSL; ^{b) %}, Amosite (RTI or UICC) or LA2000 (mixture); * TWA_{Tridymite (Mohs, 7)}, 0.5 x PEL-TWA_{Cristobalite (Mohs, 6.5)}; ^&2, ^a) Composition, MS, ICP-MS or oxygen electrochemical cell (Pb²⁺, non-oxide); ^b)x, another metal; l, length; aspect ratio, ≥ 3 (l): 1; m, min; ^c)n, x, number of repeating molecules; ^d) Inhalable dust (total), ∑ (Alveolar, Ultra-fine, < 3.5 μm_respirable; Bronchiolar, 3.5–10 μm; Bronchi (Thoracic) - Nasopharynx, ≥ 10–100 μm); and ^e) Copper-containing oxides, pure, Tenorite, Cupric (2⁺) oxide; Cuprite, Cuprous (1⁺) oxide, and Copper (II) acetate; ^&3, ^a) Detection: Gene, cDNA micro-array fluorescence, RNA-seq, gRNA Cas9-seq with qT-PCR amplification fluorescence; ^b) A) normal human bronchiolar epithelium, B) human airway epithelium, C) hematoma cell (HepG2), D) pulmonary, bronchiolar epithelium, pneumocytes (Type II); E) eosinophil; F) blood serum, human; G) chondrocyte; and ^&4, ^a) TLV, ACGIH exposure limit-TWA_{8 hr}; PEL, OSHA exposure limit-TWA_{8 hr}; REL, STEL, NIOSH exposure limit-TWA_{8 or 10 hr} (400 L air sample, 100 min), short-term exposure limit (15 min); and ^b) sampling (collection), gravimetric cassette method or cyclone-cassette assembly, 0.8 μm metal fume/asbestos (mixed cellulose ester filter), and 5 μm dusts/crystalline silica (polyvinyl chloride filter), or vertical density elutriator, ≤ 1 μm; ^▲, Appendix I, Sect. III; and Inhalable particle sizes: Aerosol particulates, Smoke soot (100 nm – 1 μm), Combusted matter fume (1 nm - 1 μm); Mist (0.1–10 μm), Dust PNOC/R (ambient inhalable, 1–100 μm; coarse PM, 2.5–10 μm, fine PM/respirable, < 3.5–2.5 μm).

The human respiratory tree accommodates a certain size range of particulate agglomerates in the breathing zone [29], and the particle aerodynamic equivalent diameter (D_ae) is the diameter of a sphere with same falling velocity (D_a) corrected for particle density (ρ). Based on the initial studies on human exposure to Amosite (A), Crocidolite (CR) or Chrysotile (CH) asbestos or glass (G) fibers, sampled and measured by the aerosol spectrometer [52], i) the fiber length/fiber diameter (aspect ratio) becomes independent of the aerodynamic equivalent diameter (D_ae, D_e)/fiber diameter ratio at aspect ratios greater than around 10: 1 and suggests that the width becomes the primary determinant of deposition, where D_e/D_f follows a fractional base-variable power function as particle diameters do not increase much for lengthier fibers; and ii) the equivalent diameter to fiber diameter relationship for three of the four fiber types is non-linear and weighted towards the equivalent diameter (A, CR, G) over the actual diameter (3.5 μm, 3 μm, 2.5 μm) but less weighted to the same in case of the Chrysotile fiber type. Thus, i) the falling velocity of aspected fiber particle agglomerates is predictable mostly by the particulate equivalent fiber diameter (D_e) that is inclusive of internal voids present in fiber aggregates; ii) particulates that possess minimal diameters are not subject to sedimentation or inertial impaction in the upper airway, and due to width dimension-weighting result in deep deposition within the respiratory tree [52]; and iii) particulate interception occurs within the respiratory tree for compact particles of different equivalent diameters than aspected particles of different lengths with earlier stage penetration within the respiratory tree for compact large size particulates, i.e. > 10 microns.

Based on review of studies 1969–1974, an inhalable particulate (IP) is defined as being ≤15 microns, and at between 2 and 3.5 microns is considered the limit by the ACGIH based on aerodynamic equivalent diameter [51] with the diameter being at 2.5 to 3.5 microns in maximal alveolar tissue accretion, and at 50% human airway penetration efficiency (P_TB, P_ET), agglomerate particle size is at 15 microns at normal flow during light exercise [29]. In the same study, the deposition of larger particles by impaction at either the laryngeal or tracheobronchial region is best-fitted by a non-linear model with co-variables, aerodynamic diameter (d_a), inspiratory flow rate (Q_total), tidal volume (V_T), and a gender- and age- category-specific scaling factor (SF_t), which models the deposition efficiency for a range of particle sizes and shows a higher deposition efficiency in the tracheobronchial (TB) region for a particle of the same size consistent with the findings of, 70%, P_{TB (tracheobronchial)} versus 50%, P_{ET (laryngeal)}.

3. Particulates characterization by shape, irregularity and charge in flow conditions

The Knudsen (K_n) variable is the mean free path length (λ, distance) to particle physical diameter (d) ratio, K_n > 0.01 < 10; and the slip correction for frictional drag velocity is a reciprocal function of the Knudsen variable [53]. The aerodynamic diameter, D_ae, to K_n flow regime transition relationship applies to non-homogenous air viscosity in flow conditions [16]; and the direct particle density (ρ_p) to dynamic shape factor (Χ) relationship (ρ_p/Χ) is applied to normalize the volume equivalent diameter (d_ve). Inaddition to particle morphology and density, the other independent variables for elutriator filtering are particle flow and charge, and include the Millikan apparatus [16].

Certain relationships are known: i) the volume equivalent diameter (d_ve) is the flow voids-adjusted mass equivalent diameter (d_me ·δ) to shape factor adjusted for standard density; ii) aerodynamic diameter (d_ae, d_a) is the shape increases with increasing particle density and the diameter with electrical charge (d_m) is larger than the d_a; iii) particle density (ρ_p) increases semi-exponentially with the dynamic shape factor, X, which is for irregular particles with no flow voids [16]; and iv) the ratio of the d_a to d_ve (d_a/d_ve) increases between continuum and free molecule air flow types (transition regime) for particulates with shape factor-normalized particulate density > 1 (ρ_p/X · ρ₀; ρ₀, 1.0 g/cm³) ratios, and it decreases in transition flow for particulates with <1 density. The electrical mobility diameter (d_m) is greater than the volume equivalent diameter (d_ve) for internal void-containing particles, irregular non-sphere and aggregate particulates with voids in-between.

As per the above discussion, i) for less irregular particulates (X < 1) there is a smaller aerodynamic diameter as per a decrease in the d_a/d_ve ratio over the flow regime Knudsen-Weber (K_n) path length (λ), and there is an increase in the aerodynamic diameter with more shape irregularity (X > 1). In Ref. to the effective particle density (ρ_eff^II) defined as the measured particle mass (m_p) divided by (i.e. normalized to) 0.125 π-adjusted spherical volume (d_m³), the relationship between the internal flow voids-adjusted particle density (ρ_p) and the dynamic shape factor is ρ_p/X, in which case an inverse relationship holds between the particle dynamic shape factor and effective particle density [15], where particle density is considered constant (k). There is also a relationship between the electrical mobility diameter (d_m) and the particle shape with the d_m increased with more particulate matter irregularity (X, dynamic shape factor > 1–2.5) [15], which is consistent with aggregation or agglomeration via ionic or non-ionic colloidal interactions, and in which case, there is an infra-log linear (saturable) increase in particles diameter (d_p) with increasing particle number (N_pp) consistent with overlapping of particles in aggregates (in solution) and agglomerates (upon condensation).

As there are lesser than expected boundary effects in the gaseous medium, there is an altered frictional drag (F_d) relation, the Navier-Stokes function with variables, coefficient of viscosity (η) and velocity of spherical particle (R_v) is modified [53], where a correction factor is applied for air velocity slip due to a lower F_d in real air flow. For imperfectly spherical nanoparticulates, the determination of particle aerodynamic equivalent diameter (D_ae) is based on the baseline normalized measure, the particle density to standard density ratio (P_p/P_o; g/cm³ fract) [16]; the effective dynamic shape factor (Χ’; F_p/F_me) for particle is the ratio of the resistance (drag) force of the actual particle to that of its mass equivalent diameter (d_me), which is the diameter for a non-spherical irregular particle un-adjusted for internal flow voids as compared to the volume equivalent diameter (d_VE); and the fraction of internal voids correction factor (δ) is for particulates that possess internal voids and/or exterior irregularity with voids. Furthermore, the determined effective agglomerate diameter of irregularly shaped nanoparticles follows a non-linear relationship for its impaction along the respiratory tree during normal inspiratory flow of 30 liters per minute.

Inaddition to the direct relationships between particle size and settling velocity (V_s) or sedimentation time (t_s), relative humidity and hydration-based exponential growth, several other aspects have been further characterized by study of virus aerosols and droplets with particle number and volume size concentration and emission small and large particle distribution (B₁, B₂; Q₁, Q₂ > 5 μm) comparison modeling of pore size threshold limits of facial PPE (fitted masks) to larger diameter particles [54], which build on earlier works on determining of flow void- adjusted aerodynamic equivalent diameters (D_ae) from mass equivalent (D_me) of a 0.125x volume spherical particle as reference.

4. Particulate matter dissolution properties and solution percolation through detritus

The atom arrangement of the dichotomous earth metal oxide morphs is polyhedral, defined by the number faces of the metal (Titanium, ²²Ti) or metalloid (Silicon, ¹⁴Si) and oxygen (⁸O) bonding, and the configuration forms for TiO₂ are anatase metastable octahedral (6 Ti, 9 O, 1, Ti, 6, 9) or rutile stable tetrahedral (8 Ti, 6 O) with differences in atom bonding ratios. The crystalline forms of oxides possess some shape asymmetry-related two-dimensional aspect (2-D, x, width, y, length), an example of which includes rutile titania and α-quartz silica (crystalline SiO₂). The solubility of the transition metal oxides varies over a range, and is measured as absolute dissolution in moles of solute per liter (M) or fractional solubility (Log S). For amorphous Silica (aSiO₂), Magnesium Oxide (MgO) and Ceria (III; Ce₂O₃), the solubility amount is 1.6e-03 - 1.6e-06 M (high solubility); the solubility of that of TiO₂, CuO and Fe₂O₃ is 1.0e-09 - 4.0e-12 M (low solubility); and solubility of Al₂O₃ and Nb₂O₃ is intermediate at around 1.6e-08 – 2.5e-08 molar [37] as compared to sodium chloride (NaCl) as an example of alkali metal halogen molecule with equivalents with a solubility of around 6.1 molar.

There are certain relationships in the solubility differences between polymorphs, crystalline and amorphous [2] based on dissolution properties of the crystalline (Rutile) and amorphous forms (synthetic) as in Titania particulates; it is a two phase dissolution, early with a steep decrease in rate of dissolution, and less of a decrease in second phase of the dissolving process. The dissolution kinetics of hard particulates are dependent on free energetics (−G_critical, α) with a decrease the critical G threshold (less negative) that favors the process with a decrease in interfacial energy (α²), specific volume (ω), and/or increase in temperature (T), in addition to solution electrolyte saturation (1 – σ). As the degree of electrolyte undersaturation (σ) increases, there is lower solution saturation, and the rate of dissolution increases, but the dissolution of all forms of hard nanoparticles, as compared to ionic particulates, is slow, with the rate constant on the order of 6 nmol per m² per sec due to the presence of the solute-solvent interaction energy barrier, a.k.a. surface tension, as the primary variable (Appendix IV. End of chapter educational objectives exercise – Particulate matter toxicity). Particle solubility can also be related by its partition coefficient (+Log P, −Log P), and its entropy of defusion as the temperature (T) · product parameter in the Gibb’s law relationship in which fractional solubility (Log S) is favored for solutes with a lower melting point (MP) [55] as per Gibbs free energetics. Therefore, there is experimentally-determined indifference in solvation rates between amorphous and para-crystalline forms related to solution desaturation, in which case electrolyte saturation favors regrowth and supports the bio-persistence of both synthetic and harder polyhedrally-bonded particulate matter.

There is the Dreiding potential for the van der Waals (vdW) forces between molecules (E_vdw), intramolecular electrostatic attractions and repulsions between atoms of a molecule (E_Q) and also intramolecular hydrogen bond energies (E_HB) in between molecule atoms, which is represented in summation form, although the three functions themselves are non-linear. In the example of aSiO₂ and cTiO₂, as to the latter two energies (E_Q, E_HB), the inter-molecular ionic bond breakage energy is 0.9 J · m⁻² [1] for the crystalline particulate and is 1 J · m⁻² for the amorphous particulate, and is similar for both as it is the aggregate-to-aggregate interaction energy. The intramolecular (internal) oxide bond cleavage energy difference for the crystalline and amorphous particulates is 10 J · m⁻² for the crystalline particulate but is 4 J · m⁻² for the amorphous particulate, and is 2.5x for the polyhedral bond configuration of the crystalline rutile [1], which has closer transition element d-orbital lattice spacing than the anatase form. The surface adsorbate-dipalmitoylphosphatidylcholine (DPPC) interaction vWV energy is 0.2 J · m⁻² for amorphous silica and 0.05 J · m⁻²; and is 1/20th of that of the bulk cleavage energy for amorphous SiO₂ and 1/200th of the cleavage energy for crystalline TiO₂, also as per the decreased bond length polyhedral bond structure.

Variables apply to water diffusion and also to the diffusion of water-dissolved solutes through a matrix, also known as percolation (Appendix IV). The diffusivity of the test substance though the matrix that can be normalized to its free diffusivity (D₀), and the experimentally-determined relationship for diffusion is modelable as an exponential decay function with a respective decrease and increase in solution diffusability with effective molecule size (a) and matrix pore size (r_m) [56]. In the modeling of the relationship percolate diffusability through porous media or matrix, there are differences in relationship between percolate (p) permeability or diffusability (D(p)/D₀) and matrix porosity (φ)-normalized diffusability of water content through the matrix (D_s (θ) / D_s (φ, phi)). There are both lesser and greater slope, linear and non-linear approximation, fits between solution diffusability and matrix volumetric water content (θ range, 0.0–0.5 a.u.) with cross-overs (θ_X), 0.0–0.08 (linear fit model), 0.08–0.145 (non-linear fit), 0.145–0.155 (linear fit) and 0.155–0.195 (non-linear fit) [57]. Thus, the intervals of the quasi-linear and -exponential relationship are modelable by the power law with different base (x-axis) and exponent (slope) variables in which percolation (p) is related by the fraction of occupied bonds during filtering flow and the irregular or regular lattice pore filter threshold at critical bond occupation probability (p - p_c, ∆ p) to the power law rule variable (t or q) [58] in which a decrease or increase in diffusion is modelable with q < 1 (infra-linear) or with a q > 1 (supra-linear), while an indirect (inverse) linear relationship between pore ionic interaction coordination number (Z) and critical percolation threshold limit (p_c) exists in three-dimensional systems.

5. Bioengineered particulates

The particulates include the nano-sized small molecule amphiphile-coated iron oxides (8.9–16 nm), the size tunable wavelength-emission quantum dots (5–18 nm), i.e. CdSe interior, ZnS shell ± RGD-Lys, and proteolytically-degradable protein- aggregate suspension xenobiotic forms such as Abraxane (130 nm), or liposomal formulations of the same [59], size range 100–300 nm to 580 nm [60], with sustained drug release kinetics for biomedical applications and in in vivo circulation or as lower distribution size fractions (Table 2. Bioengineered nanoparticles). Engineered particles have increased surface area with capacity of exterior surface polyvalency by covalent bound formation to free exterior terminal groups such as amine- or carboxyl- with 2^x exterior terminal groups per n^th dendrimer generation and potential for conjugation by covalent linkages; and in addition to the Neocarzinostatin poly(styrene-co-maleic acid) conjugate (SMANCS) [78], the soft nanoparticles include dendrimers and poly-(lactic-co-glycolic acid) (PLGA) in monodisperse size distributions within the NP size range [71], and also include the emulsion- and liposome-based that are approved for human use [70]. By covalent linkage of high-MW polyethylene glycol (PEG) for stealth properties, biocompatibility and blood plasma half-life are improved with a shift to either hepatic and/or splenic RES sequestration [8, 9, 70, 91]; and while soft nanoparticle size and exterior percent conjugation can be tuned by divergent synthesis in comparison the non-covalent type by affinity matching for functionalizing of emulsion- or inclusion-type nanoparticle phospholipid head groups, and emulsion polymerization in case of reactive end group types (i.e. cyano-acrylate monomer).

Bioengineered particle	Example(s)^&1	Formula^&2	Overall or effective particle size (x10^−9 m), Mean or range	Physicochemical properties –Exterior/Interior	Application(s)	Ref.
Soft
Dendrimer	PAMAM, Poly-L-Lysine, Polyester bowtie	2^n	1.5 (Core), 2–14 (G1 – G8)	Cationic (terminal amine)/DAB, EDA*; Anionic (chelate, Succinate)/*, Ionic Neutral (Benzene disulfonate)/*	(T1^−1, per msec)-low relaxivity (r1^−1) contrast MRI (passive EPR / active)	{Jackson, 1998; Tomalia, 2007; Rupp, 2007; Wang, 2022; Sarin, 2021; Kaminskas, 2011; Olson, 2022; Sousa, 2009; Kobayashi, 2003; Banerjee, 2011} [17, 61, 62, 63, 64, 65, 66, 67, 68, 69]
Liposome	Doxil, Myocet	X-(PEG5 kDa)n	100–580 nm	Phospholipid bilayer (±PEG), polar periphery, apolar core	Stealth imaging, EPR	{Prabhakar, 2013; Pillai, 2013} [59, 70]
PLGA	Disulfiram Folate nanocapsule	PLGAn -N(H)OC (PEG5 kDa)n- N(H)OC-sm. mol. · sm. mol.	1.5–10 [(PLGAn)1-drug]1; 150–210, 0.5 μm (polymer)	D, L-lactic acid, glycolic acid – Folate (c.), Disulfiram (n.c.)	Polydrug targeting	{Danhier, 2012; Fasehee, 2016; Makadia, 2011} [71, 72, 73]
Carbon nanotube (SWCNT)	SWNT-l-, −br-PEG	(Cyclohexane)n · [C(O)O]2-(CH2)2(PO4)-NC(O)-(PEGethyl ether)n	5–10 (w.)	Poly-cyclohexane · PEG phospholipid (n.c.)	Raman imaging, passive EPR*	{Liu, 2008; Singh, 2006; Kotagiri, 2014} [74, 75, 76]
Uni-micelle	PEG-IR780-C13	(PEG)n-IR780-(C)13	≤ 120	Phospholipid layer, apolar periphery, polar core	Imaging, passive EPR	{Åslund, 2022} [77]
Suspension	Abraxane	(Taxol, f.f.)n · (Alb6.9 nm)m	130	Taxol tetra-ester (Paclitaxel) · Albumin (n.c.) · (Y solvent)	Nano-sized suspension EPR	{Prabhakar, 2013; Pillai, 2013} [59, 70]
Inclusion emulsion	SMANCs	NCS-(SMA)2	3.75–5	Neocarzinostatin-SMA2 · Lipiodol (n.c.)	EPR	{Tsuchiya, 2000; Deschamps, 2017; Ahnfelt, 2019; He, 2022} [78, 79, 80, 81]
	PACA	(Taxol, f.f.)n · (ACA)m	50–130	Cabazitaxel ·PEBCA (n.c.)	EPR, fluoro-imaging	{Åslund, 2022} [77]
Colloidal	Gold	(Au)n	10–50	Non-polar inorganic atom (n.c.)	X-ray imaging, Gold radio-therapy	{Dykman, 2019; Arnida, 2011} [6, 10]
Shape polymer	MPE	(PEGn)m(PE)o-Php	2–12 μm	Isophilic / non-polar organic atom (Polyethylene glycol4)2-(erythritol phenyl)2 (c.): PVA (n.c.)	Matrix fabrication / Bioapplication	{Kinbara, 2018} [82]
Hard
Iron oxide	VSIOP, SPION	(Fe2O3)n^	8–30	Fe2O3 core: polymer coat (n.c.): PEG (c. with stabilizer)	(T2^−1)-high relaxivity (r2^−1) contrast MRI	{Nabavinia, 2020; Bulte, 2002; Wu, 2015; Jeon, 2021} [25, 26, 83, 84]
Quantum dot	ZnS, CdS, ZnSe shell QD	(CdSe)n	2–10	Valance band core: shell (n.c.):: PEG (c.); Inverse band gap size-dependence	Semi-conductive transmission (non-band gap); Narrowband fluorescence emission (Red, 7 nm; Light blue, 2 nm)	{Schipper, 2009; Choi, 2007; Dhenadhayalan, 2018; Singh, 2019} [8, 9, 85, 86]
Aminosilane silica-coated iron oxide	SIONP, CLIO	(C2O2)n)5 kDa -H3SiNH2 [SiO2 (am) [(Fe2O3)n]	111–202 (whole); 9.7 (core); 8.5 (shell)	Isophilic PEG 5 kDa – amphiphilic aminosilane coat (n.c.): Silica shell (n.c.): iron oxide	Stealth; High r1/r2 (T1W MRI); High r2/r1 ratio (T2W MRI)	{Bruce, 2005; Bumb, 2008; Mathieu, 2019; Iqbal, 2015; Wunderbaldinger, 2002} [7, 87, 88, 89, 90]

Table 2.

Bioengineered nanoparticles.

^&1 Polyamidoamine (PAMAM) DAB core, diaminobutane (C₄H₁₄Cl₂N₂); EDA core, ethanediamine (C₂H₈O₂); MPE, monodisperse polyethylene; SPION, supra-paramagnetic iron oxide nanoparticle; PLGA, poly(lactic-co-glycolic) acid; PEBCA, poly(2-ethylbutyl cyanoacrylate); CLIO, cross-linked iron oxide ^&2where x = n + 2, and n = 0, dendrimer G₀ with 4 terminal amines, n = 1, dendrimer G₁, 2³ = 8 terminal groups; and ^{^}Iron-containing oxides, Hematite, α-Fe₂O₃ (rhombohedral); Magnetite, Fe₃O₄ (cubic), Maghemite, γ-Fe₂O₃ (tetragonal); PVA, polyvinyl alcohol; EPR, passive enhanced permeation and retention, or targeted EPR at *some CM receptor or channel; n.c., non-covalent; c., covalent; l, linear; br, branched; w, width; and X, head group (zwitterion, carboxyl or amine, i.e. NH₂-PLGA-NH₂; sm. mol., small molecule

Generation of uniform distributions of hard nanoparticles is by either bottom-up methods such as by chemical reduction and molecular condensation of multi-atom nuclei [6], or by top-down bulk mechanical milling for example [92]. In addition to the growth of polymorph-bonded atoms, further coating of the surface layer (shell) is required for exterior biocompatibility such as for quantum dots [8, 9], iron oxide [7], silicon dioxide [87, 88], emulsion and colloid type [88]. The size and shape of hard nanoparticles can be further modified by differing reaction times, concentration, and via thermal decomposition [83] followed by encapsulation of dispersed monomeric particles by surfactant addition and sonication with resultant droplet formation containing magnetite particles (Fe₃O₄) as an example of methods applicable for high MW PEG-stealthing of simple phospholipid bilayer liposomes with interior hydrophilic contents [70]. Functionalization of nanoparticles is by surface layer and condensation reaction such in synthesis of silane coat-bonded PEG on SiO₂ encapsulated Magnetite cores (Fe₃O₄) [83], or by the ethero-isophilicity of the exterior surface PEG portion of the dialkene (i.e. 15-C) diphosphorylo- group covalently bound to PEG for shift from renal clearance [74, 75] and stealth property to limit opsonization [76]; the interaction with the SWCNT itself is non-covalent with the alkane part molecule with similar apolarity affinity for the SWCNT wall with dimensional aspect and a + Log P lipophilicity. The degradation of the covalent linkage is hydrolytic time constant dependent for the dendritic conjugates, it could be that earlier disassociation of the two portions of the similar partition coefficient (P) results in SWCNT nanoparticle opsonization and splenic accumulation, and the free alkyl chain phosphorylo-PEG portion could result in delayed sensitivity by B-lymphocyte IgM, IgG or IgA immunoglobulin response. The less biocompatible polymers include the more hydrophobic, and large diameter hexagonal polymers such as 6·(PEG₄-erythritol) phenyl group type ([82], Table 2) that instead have applications in matrix fabrication, and the polyvinyl chloride (PVC) PEG proportioned crystalline-amorphous composites that have lower transition, melting temperatures (T_m, DSC) [93] and are more deformable than PVC with uses in plastics.

5.1 Dendrimers

The sub-classes of dendrimers include amido-amino dendrimers with core ethylene diamine, (EDA) and diaminobutane (DAB) with amine, carboxyl, hydroxyl or polyethylene glycol (PEG) terminal groups, Table 2). Naked heavy metal dye-stained PAMAM dendrimers range in between 1.9 (G₁) – 9.8 (G₈) nanometers, and with DPTA-/DOTA-chelate functionalization range in between 12.7 ± 0.7 and 13 ± 1.4 nm (Gd-G8; Rh-, Gd-G8) with the number of Gd at 350 atoms per dendrimer (Gd-G8). Inaddition to the monodispersity and narrow size distribution as evident with (Na_n) phospho-Tungstate staining by catanionic affinity [17], terminal PAMAM group synthesis is an applicable property as it results in polar molecular anisotropy for DNA van der Waals (vdW) ionic affinity; as result, it is neutral surface nucleic acid transfection by electropolation [61]. The naphtha-sulfonate functionalized Lysine amino acid-based G4 dendrimer [62], BHA.Lys₁₅Lys₁₆(NHCOCH₂O)1-(3,6-naphth(SO₃Na)₃₂ (BHA = benzhydrylamin), MW 16.6 kDa, is an applied gel sol barrier cream with overall neutral surface and low risk for nanotoxicity; and most recent, there is a polyfunctional group inseries dendrimer hydrogel with internal PEG linker (oxyethane_n) and hydrolysis-sensitive ester linkage for improved degradability and less toxicity [63].

The surface anionic PAMAM dendrimers are the half-generations (-COO⁻: G1.5, n = 16; G3.5, 12.9 kDa, 5.2 nm; G5.5, 52 kDa, 7.9 nm), and by small angle scattering (i.e. SANS) are spherical shape in deionized solution with low polydispersity (PDI); and there are certain surface physical interaction properties that are associated with anionic macromolecule tissue deposition: i) Caveolin-1-associated protein (AP)-mediated endocytic uptake component exists at higher concentration (0.13 μM) since there is lung tissue deposition at lower concentration (0.020 μM) for example in the case of AT1-like alveolar cell/macrophage; and ii) inter-epithelial cellular permeability decreases with increasing half-dendrimer generation between 7.9 nm and 9 nm (FITC-anionic dextran-70 kDa) due to the junctional complex pore size threshold. A cationic or anionic exterior results in interactions with cell surface receptors or channels with varying affinity, and also results in uptake via internalization mechanisms by non-linear rate kinetics at binding, which is evident by the less than expected blood serum half-live (t_1/2) to plasma/cell surface interaction as in the case of certain dendrimer types. Since an earlier blood t_1/2 than expected for the mass density size product of a nanoparticle is consistent with rapid non-selective internalization, this pharmacokinetic parameter can be considered an indicator of toxicity potential [64]. In contrast, the benzene disulphonic acid (BDS) dendrimer ionically-neutralized anionic exterior dendrimer (near neutral) has extended blood half-life [65] with enhanced passive permeation accumulation in tumor tissue. Mixed-surface charge -type dendrimers with less effective cationic charge per area for higher affinity binding of cell- and pathogen-released nucleic acid and molecule byproducts, which decreases inflammation pathway activation and toxicity by D- and P-associated molecular pathways (AMPs) with maximum potency and efficacy of deactivation effect for G4 50:50 dendrimers with equivalent terminal amine and hydroxyl groups [66].

High-resolution characterization of Gadolinium (Gd³⁺)-chelated mass-dense dendrimers is by transmission electron (TEM) with a combination of low- and high-dose diffraction techniques, annular dark field (ADF) STEM and energy-filtering (EFTEM) [67], for contrasted detection at high resolution of macromolecule mass and the number of heavy atoms as in the example of Gadolinium (Gd³⁺)-chelated (DTPA⁵⁻) monodisperse nanoparticles without the need for heavy metal, i.e. Osmium (190 Da) tetraoxide (OsO₄) staining for visualization for a distribution with a median frequency of between 25 and 30 individual nanoparticles within a size range of 12.7 ± 0.7 nanometers (nm) for the Gd-generation 8 dendrimer (600 kDa). With neutral surface nanoparticles there is limited toxic potential, and the mechanism for improved efficacy is by the Maeda effect of passively-enhanced accumulation and retention (‘EPR’) for tumor treatment is based on blood capillary pore size selectivity [78], and further improvements by conjugated exterior ligand targeting of cell surface receptor overexpression or receptor-specific monoclonal recombinant IgG (12.6 nm)-based therapeutics with non-renal, hepatic and/or splenic clearance-dependent circulatory half-lives. As far as macromolecular imaging agents of this class for macromolecular imaging by dendritic conjugates are concerned [68], cyclic chelate Gd-DOTA poly-Lysine dendritic architecture conjugate, Gadomer-17 kDa, is a clinical use due to the concern that acyclic chelates such as diethylenetriamene pentaacetate (DTPA) have the potential for toxicity in vivo due to free Gd³⁺ ion release from chelate. As paramagnetic atom (i.e. Gd³⁺) chelation affinity increases, water relaxation rate decreases per M*msec, which results in lower contrast enhancement intensity.

5.2 Micro-emulsion and inclusion emulsion

Microparticles with insoluble components dispersed in or out of a mixture that scatter light are either: i) colloidal are defined as homogenous non-crystalline matter dispersed into the other but non-sedimentable, and also consist of elements in their unionized element core form in self-association for example in a medium or in deionized water; ii) an emulsion as a colloid with single layer exterior phospholipid coat (amphiphile); or iii) a suspension with sedimentation possible of one of the substances.

Lipiodol (ethiodized oil) is an ethyl ester of iodized fatty acids with the other lipophilic component a single layered amphiphile stabilizer with 1% of poppy seed oil linoleic and oleic acids, a simple form of emulsion. The toxicity profile of Lipiodol use as radiopaque contrast agent for angiography or lymphography is related to: its rate of infusion and risk for fat emulsion embolism [79], and to it lipophilic constituent composition that results in delayed type hypersensitivity response (Type IV) with repeated administrations. Emulsion or emulsion inclusion type particle size distributions depend on water: oil ratio and are within the millimeter (mm) droplet size range (Table 2). The ethiodized oil-in-water (immiscible), or water-in-oil (miscible), emulsion is the two liquid emulsion form that is made a more complex drug-inclusion type emulsion containing less hydrophilic drugs such as Doxorubicin (Daunorubicin, Log D/vdWD: −0.74 nm⁻¹) to improve the drug half-life and effectiveness; it is the water-in-oil emulsion that is stable and the improved systemic toxicity profile is by slow continuous infusion of the 62.5% water-in-oil emulsion showing statistical significance by categorical comparison [79]. Doxorubicin (Dox) has greater solubility in Iohexol (75 mg/mL) than in saline (50 mg/mL) than in ethiodized oil; the miscible 1:4 aqueous-to-lipid phase ratio has low stability and disperse distribution of particle sizes; and the release constant (K_rel; per hr) is most favorable for the 1:4 saline-in-lipiodol phase ratio form [80]. The formulation can be made more stable by inclusion of nano-sized hydrophilic NPs such as the PLGA monomers [81] that is tested for its in vivo properties such as in the rabbit HCC model in which it is shown that the homogenous intermixed emulsion inclusion Dox form (SSIF) with initial polymer droplet size 100–150 nm (non-covalent, n.c.; Table 2) has improved stability by which the Dox (C₂₇H₂₉NO₁₁, MW 544 Da) in PLGA monomer association is more effective than the traditional iodinated formulation (TIF) through blood-tumor barrier (BTB) capillary endothelium fenestrae-interstitium with pore size upper limit 12 nm (un-hydrated particle size/TEM-based; [64]) .

Other emulsion inclusion-based nanostructured lipid carriers also include the PACA sub-class PEBCA (Table 2) with more and less lipophilic phases and Cabazitaxel inclusion [77] with low-loading dose renal clearance and high-loading dose (higher MW size) splenic clearance over hepatic due to improved plasma half-life (t_1/2); and PEG-IR780-C₁₃ uni-micelle (LipImage) has temporal biodistribution kinetics in comparison, first into liver and then to spleen, both tissues being RES cell tissues. The remainder of such soft nanoparticles include the targeted with drug delivery capability based on the poly-lactic glycolic acid (PLGA) repeating unit hydrophile structure and hydrophilicity-based non-covalent polymerization affinity (150–210 nm) [72] with the potential for site bioactivity in vivo in monomeric form (1.5–10 nm).

5.3 Silver and gold colloids

Colloidal particle synthesis is by mixture of an ion solution and hydrogel with applied heat, adsorption upon reduction of oxidation potential, and can be of defined sizes [6]; if uncoated, then surface oxidation results. The colloidal Silver nanoparticles, AgNP_{20 nm} and AgNP_{70 nm}, has been studied in the human eosinophil cell culture model [39], and of the two groups, the smaller NPs with greater surface area-to-volume ratio (AgNP_{20 nm}) result in a wide range of sizes in media solution, i.e. 158.9 nm (μ₁) – 1482 nm (μ₂) secondary to aggregation by DLS, while the AgNP_{70 nm} have a lower left-side distribution size (114 nm) and do not aggregate to the same extent due to less oxidizable surface area available for assoc. to the anionic composition of the media. Of these two size distributions of colloidal NPs, only the AgNP-20 nm show an increase in cell apoptosis by Annexin-V staining confirmed by pro-Caspase-3 and -7 decrease, inaddition to Laminin B1 filament protein cleavage; thus, the difference in the potential for cell apoptosis of colloidal elements is related to colloid element oxidation state, and available aggregated colloid anionic surface area for cell surface interaction.

Concerning the size difference between TEM measurement and DLS in the case of the AgNPs, for example the AgNP (NM300K) it can be noted that these are 7.75 ± 2.48 nm (μ ± σ) in diameter, while size range in solution even after dispersion for this set of colloidal NPs with a much narrower dry distribution (AgNP_{8 nm}) than the AgNP_{20 nm} that aggregate is 28.71 nm or 38.46 nm (μ₁, μ₂), and 81.37 nm or 97.23 nm (μ₃, μ₄) in media [39], which varies with concentration (μ₁, μ₂) and time (μ₃, μ₄) in solution. For this particle distribution, the aggregation is less skewed the effect remains cell viability decreases with concentration dependence with more of a decrease in viability for the Beas-2B immortalized cell line over the A549 bronchial adenocarcinoma cell line; and due to the poor solubility of AgNP with a less electronegative electrical potential to ionize as per the Pourbaix-pH relationship. Cell viability percent (%) assay, and Comet DNA tail sign assay for chromatin DNA genotoxicity, and cellular oxidative stress-determining assays such as the FPG enzyme assay for oxidized base carbon detection (i.e. oxo-Guanine) or SOD activity and GSH level reduction assays are utilized for determination of such effects [94, 95].

The toxicity of PEGylated colloidal Gold NPs, in the form of spheres or rods, has also been characterized with size and shape determined by TEM, which is similar in length dimension (50 nm, 45 x 10 nm) [10]. The nanospheres are monodisperse with a PDI of 0.02 and with a negative zeta (ζ)-potential (−27.1 mV) and agglomerate in solution as per a size of 89 nm by DLS and this in vivo would be by association of Citrate impurity, or could be due to association with the R-groups of native albumin (pI = 4.5, anionic), whereas rod-like NPs with aspect will remain near neutral ζ (+1.13 mV). It appears that the presence of surface charge is the primary determinant to particle clearance in vivo, which limits tissue interstitial toxicity since the effective exterior anionic spherical particles are hepatic > splenic RES substrates, while the rod-like particles with slight exterior positive charge and 1-D aspect smaller than the tumor tissue pore size accumulate passively in tumor tissue. Based on a recent study of the biodistribution of AgNP or AgNP and AuNP of similar sizes (log-normal, μ = 10.82, 10.86, GSM) after co-inhalation exposure to rodents over 28 days [96], at day 1, the AgNP aggregates distribute to the liver and olfactory bulb over the spleen; whereas, with AuNP co-inhalation, the biodistribution of the Silver (Ag, ng/g) is to the spleen over the liver, and is due to the opsonization of the particles, larger effective sizes and retained permeability to the splenic arterial side capillary beds of the red pulp; this is consistent with the more lipophilic and less soluble character of the Gold elemental/colloid particle that results in its opsonization and splenic accumulation after co-inhalation exposure.

5.4 Iron oxide nanoparticles

The iron oxide NPs (IONPs) have supraparamagnetic water proton-relaxation properties and include the ore-derived Hematite (Fe₂O₃; Fe^III, 2), and also the chemically-synthesized crystal Magnetite (Fe₃O₄; Fe^II, 1: Fe^III, 2) form by basic pH ultrasound-assisted co-precipitation of ferric and ferrous chlorides [83], with both iron forms containing unpaired electrons reduced by mono-Oxygen. The various IONPs are (size range): SPIONs (50–180 nm), ultrasmall PIONs (USPIONs, 10–50 nm) and the very small SPIONs (VS-SPIONs <10 nm) (Table 2) The IONPs are r₂-molar relaxivity contrast agents with above paramagnetic effect relaxational rate properties (R₂) that result in T₂-weighted contrast enhancement with percussion frequency around transverse magnetic plane as compared to the Gd³⁺ and Mn²⁺ transitional metals with T₁ shortening, and longitudinal relaxation rate (R₁) increasing magnetic properties per concentration of agent per time (M · msec)⁻¹ with a reciprocal size-dependent enhancement effect high-molar relaxivity (r²) paramagnetic effect contrast enhancement for long T_R, long T_E sequence MRI (T₂W-seq) in the inverse correlation IONP size-to-relaxation rate effect [84], and a direct relationship between loading amount and magnetic strength [26]. Silica-coated Magnetite particles also developed with nanoporous silica exterior shell with an aminosilane organic coat for PEGylation and multimodality applicability [7] of either a low or high contrast enhancement molar relaxivity ratio (r₂/r₁) [88, 89] (Table 2); there also exists a class of cross-linked iron oxides (CLIO) in core for improved stability of exterior surface for amination (CLIO-NH₂)_n for additional covalency and functionalization [90].

Iron oxide NPs of spherical shape by SEM, and mean size 60 nm by TEM, result in lymphocyte cell viability decrease in an inverse relation to ROS and toxicity upon accumulation, which is responsive to applied Thymoquinone (TQ, ox) [95], a Phase II detoxification enzyme (NQO1) substrate that metabolizes to cyclohexa-2,5-diene (methyl, isopropyl)-yl-1,4-diol, with maximum reduction in ROS at mid- concentration in a low, high concentration parabolic relationship of ROS production, and alike to the toxicity type that results in A549 adenocarcinoma cells upon exposure to ZnO NP (NM110) in which oxidative toxicity is more than genotoxicity [94]. Although the potential for interaction with the nuclear DNA exists as determined by UV (200–350 nm)-visible spectra- scopy, particle toxicity for such less soluble crystalline particulate matter is primary non-genomic extranuclear toxicity. For bioengineered iron oxide NP infusions, the in vivo pharmacokinetics and biodistribution is measured by QAR following ⁵⁹Fe radiolabeled-AMI SPION NP infusion, which results in an increase in transverse tissue relaxivity (R₂) with T₂W-negative contrast enhancement in liver over the spleen or lung organs [97] and is by hepatic macrophage-reticuloendothelial system cell internalization.

Endogenous iron in its free oxidized form (Fe³⁺) readily reduces, and in its hydroxylate ferric form bonds into Apoferritin (18 nm), a variable diameter multi-α-helix 24mer subunit cage (MW 474 kDa) that can hold 3.5–4 Fe (II)/O₂ with between 220 and 1900 (− 2220) molecules of >50% oxidized ferrous iron (Fe³⁺) [98] as Ferrihydrite, Fe³⁺O₂H · nH₂O [99]. Accrual of iron oxide densities is seen on TEM of pollution-exposed population sample specimens with inelastic scattering EELS spectra showing the iron oxide-containing Hematite, Magnetite and Goethite nanoparticle forms (30–50 nm) [100], some of which, could be Ferritin protein-assoc. forms such as 2 L-Ferrihydrite/ABACA, and Maghemite-like that are neurotoxic and also visible by nano-diffraction TEM of small volume samples [101]. Inaddition to epithelium- and olfactory nerve ending- internalized exogenous particles, the toxicity of the oxide particulates is also from extracellular bioaccumulation potential due to microglia-macrophage overload and deposition as it is in the case of Ferritin adsorption on inosilicates and formed asbestos bodies in tissue interstitium.

5.5 Cesium-, zinc- and cobalt- oxides

Inaddition to the effects of AgNP-20 nm and AgNP-70 nm particles (Sub-section B), the effects of other major oxide forms of the other transitional metals have also been studied in the human eosinophil cell culture model, and include the rutile and crystalline forms, TiO₂ (anatase crystal oxide), CeO₂ (crystalline) and ZnO (crystalline) NPs particles have been also studied in the human eosinophil cell culture model [39]. The respective particle aggregate populations follow a multimodal distribution, but could also fit a log normalized distribution as a single population per DLS after particles dispersion in cell culture medium such as RPM1 1640 with particle size measured in-solution by dynamic light scattering, and in the examples of CeO₂, ZnO and TiO₂ in which the log-normalized CeO₂ particle distribution is left-shifted as compared to for the other two with a smaller particle size distribution; and as to the cell viability and gene expression effects, the elemental oxides with favorable electro-potentials to maintain in dry/TEM particle size (TiO₂-NM101), or decrease in size upon de-aggregation (CeO₂) have less potential for negative effects on cell viability and toxicity [94], and of these three oxides, the effect of the largest dehydrated particulate of the NM series, ZnO-NM110 (132 nm, TEM) is towards ROS generation [94] and apoptosis [39]. The molecular gene expression changes that occur as a result of cell internalization include the activation of IL-8 and MIP-1α/−1β (ZnO, AgNP _{20 nm}), RANTES (ZnO) genes with non-change in CXCL9 (alias MIG) gene and cytokine levels gene expression [39].

The genomic effects following the local intratacheal instillation of Cobalt NP (Nano-Co, 50 μg/mouse; μ = 20 nm, TEM) and Nano-TiO2 (μ = 28 nm) have been studied in the mutant guanine phosphoribosyltransferase (gpt) assay for selection of positive Cre-activated cell clones with integrated transfections by application of 6-thioguanine to cells in culture, and crossed transgenic mice study endpoints [40]. Based on such study data, it can be specified that: i) there is an increase in local protein concentration due to increased permeability of capillaries, and decreased neutrophil number is due to cell diapedesis inaddition to an increase in local inflammatory cytokine (CXCL1) concentration; ii) there is an increase in genomic DNA base transversions (G- > T) with Nano-Co colloidal Cobalt suspension exposure, but not with control or Nano-TiO₂ exposure as determined by 8-OHdG levels and non-WT gpt gene single-pair base sequence mutation frequency analysis; and iii) there is cell cycle progression upon exposure to Nano-Co that surface oxidizes (Co²⁺) with the potential for assoc. anionic serum/plasma constituents, and can be seen in lung parenchymal cells by IHC staining for PCNA and Ki67 (MKI67) positivity as compared to Titania (TiO₂) that is reduced and in oxide form. There is a probability that there is correlation to bond dissolution rates as the water solubility (mol_metal/L) of titanium- (Ti₂O₃, TiO, n/a; TiO₂, 10⁻⁹ M) and iron- oxides (FeO, Fe₂O₃, 10⁻¹⁰ – 10⁻¹² M) is low [37], the oxide-types being Ferritin cage substrates; and the potential for inflammatory/ROS stress, DNA alterations and cell proliferation/transformation exists with high concentration exposure to transition metals of oxides that have higher solubility.

5.6 Titanium oxides

Cell membrane (CM)-associated cellular esterase-mediated dichlorofluorescein diacetate (H₂DCFDA) acetate cleavage and reduction (H₂DCF) assay is coupled to the with DCF oxidation fluorescence assay for ROS detection; by the cell culture-based ROS generation assay, amorphous and rutile TiO₂ particle matter reactivity is determined by size and crystal phase [41]. TiO₂ samples are prepared by aerosol reactors, particle size distributions differences are generated by spectrometry (SMPS), and morphologic characterization of the size distribution is by filter paper electron microscopy (SEM, TEM) and by x-ray diffraction (XRD) for particle size characterization of particle distribution phases. There are monodisperse distributions of amorphous phase TiO₂ particles within the >30 ≤ 53 nm size interval are most reactive in peroxide generation (H₂O₂) per particle surface area (S.A., μmol/m²). Particle number and concentration are the variables for 3 nm-sized particle-mediated ROS, while 41–53 nm particle size is favorable to cell ROS generation in the order of amorphous > para-crystalline (anatase) > rutile for a 34–102 nm range particle size distribution. In this study design, particle number and concentration are the additional variables to particle size with increased ROS stress determined for the amorphous particles in a single time point experimental data acquired at 15 min. in which case solubility differences would be negligible between the particle types.

The dose-dependent effects of the anatase and crystalline forms of TiO₂ particulates are assessed in the 3 T3 fibroblast cell culture model exposure model by nuclear division index (NDI) and the Cytochalasin B F-actin inhibitor binucleate micronucleation chromosomal damage (BNMN) assay with non-aggregated mean particle hydrodynamic size ranges (DLS) determined in cell culture medium for nano-sized anatase (An-10; 26 nm), bulk anatase (B-An; 260 nm), nano-sized rutile (Ru-10; 82 nm) and bulk rutile (B-Ru; 755 nm) [42]. There is maintained cell internalization to a minimum of 28 nm (An-10; > 28 ≤ 53 nm) for anatase forms (TiO₂) with no change either assay (BNMN, NDI), however this is not the case for the rutile forms, which have a right-shifted bimodal size distribution, aggregate, and are present in the media supernatant rather than within cells at 24 hrs; the B-Ru particles either endocytose less, or endocytose initially. The 82 nm-mean size Ru-10 crystalline particles are more oblong in shape (TEM) with a one-order more negative zeta (ζ)-potential (DLS) than the egg-shaped/spherical An-10 particles.

5.7 Quantum dot nanoparticles

The intermediate group metal-metalloid QDs with semiconductor properties have been applied for biochemical luminescence detection [85, 86]. The quantum dots are comprised of a transition metal (Group 12; i.e. Cd, Zn) and metalloid (Group 16; Se, S) bonded atoms as core (CdSe) and shell (i.e. ZnS) particles with conduction to valence band orbital fluorescence emission (E_{m λ}), Table 2. Being of monodisperse nanometer scale size distributions, the particles have applications in electronics including for LED photoluminescence (PL). QDs with Tungsten Sulfide (WS₂) core have a disperse distribution with one modal interval in-between 4 and 6 nm, and absorbance (λ_abs) wavelengths at 246, 278, 333, 365 nm and E_{m λ} in-between 370 and 500 nm wavelengths with this range being conducive to wide-range of emissions color application. As to the shell and organic coating, there is a concentration-dependent cation interaction-mediated decrease in PL intensity applicable to the detection of a number of milieu transition metals (Pb, Cd, Hg, Fe³⁺ (Fe²⁺), Cu) [86], and also in other functionalized organic molecule-coated QDs such as Molybdenum Selenium (MoSe₂/COO⁻, NH³⁺, SH) consistent with ionic chelate neutralization that results in an increase in emission intensity (i.e. Cu²⁺/COO⁻) [85], which also affords efficiency in detection by threshold-based binomially. PEGylation- and peptide-incorporated quantum dot-based NPs with larger size (> 12 nm) with increased blood circulatory t_1/2 time and Stokes shift emission for PL at λ_{800 nm} (NIR) [8] with applicability for effect on only irradiated tumor tissue angiogenic capillary-interstitium barrier pore size that can also be studied by other small NPs with monodisperse distributions in the lower nano-size range NPs [102]; and the initial feasibility of targeting to vascular malformations can be studied by endothelium-targeted QDs nanoparticles (i.e. α_V β₃ integrin, [69]).

The size distributions of Cys-QDs have been shown to be narrow per QD color (E_{m λ}, 515–584 nm) and within the 2.85–4.31 nm range by TEM for dry particle size, and within the 4.36 (GFC), 4.64 nm - 7.22 (GFC), 8.65 nm (DLS) range by size exclusion chromatography and dynamic light scattering in solution {Choi, 2007 #9} [9]. There is a difference in aggregation potential for particles with non-neutral exterior properties, but indifference in size for zwitterion ion organic molecule-coated shell wet QDs inaddition to those in solution with DHLA-PEG coating and near neutral exteriors. Cys-QDs show renal elimination clearance after direct infusion with threshold limits determinable by bladder intravital fluorescence microscopy (IFM) intensity; there is an increase in blood half-life (t_1/2) beginning at a hydrodynamic diameter (HD) of 4.99 nm [9], which is consistent with the pore size limit to peritubular renal capillary secretion. The pharmacodynamics of tissue distribution for neutral charge QDs with larger diameters (8.65 nm) and zwitterionic surface is hepatic > lung (> spleen), and that of smaller particles (4.36 nm) is a slight shift towards hepatic elimination due to the renal peritubular capillary reabsorption pore size threshold; splenic RES cell clearance is for non- cationic, −anionic or -cationoneutral micro-sized particles.

5.8 Pharmacokinetic models and hyper-permeability analyses

The circulatory transcapillary transfer potentials (P_c, P_i, π_c, π_i), reflection coefficient (σ) and permeability coefficient (L_p) are the three variables in the relationship for hydraulic flux calculation; first the forward transfer water flux per unit area (J_v/A) is determined, and then the ratio of the rate of solute flux-to-rate applied of water flux ratio (J_s/J_v; a.u.) is determined [103]. Since hydraulic or water conductivity coefficient (L_p)-adjusted flux is affected by the presence of circulatory macromolecular particulate matter, there is the Peclet variable adjustment in the modified Starling relationship [104]; Appendix I. Microvascular fluid exchange and pharmacokinetic modeling); and the relationship is also applicable for determining of hydraulic flux of water (or solute in Ref. to water) in the presence of pharmacologically-applied macromolecule interactions in modeling secondary to exterior vdW hydrophilicity interactions of capillary wall permeable macromolecules with water; there are two relationships, one that is of hydrostatic pressure favoring filtration when there is a lower Peclet coefficient, and the other of the effect of the permeable fraction of the macromolecules and increased oncotic effect on water filtration. The overall model for transcapillary permeation is a three layer plus one layer barrier [105] in which there are three capillary layers (EGL, endothelium, basement membrane) with the fourth layer being the interstitial matrix, and at which vdW interactions occur.

Endothelial barrier hyper-permeability and the altered pharmacodynamics of affected tissues can be also be studied in vivo by real-time quantitative MRI (qDCE-MRI) at high resolution and analyzed by contrast enhancement-based multivariable compartmental modeling with several parameters, C_t (t), C_p (t), V_p, K_ep and V_e and the time (t), time constant (τ)-dependent fractional solutions for the forward transfer constant, K_trans, are determined (Appendix I. Microvascular fluid exchange and pharmacokinetic modeling). The pharmacokinetic toxicity assessment-applicable models have the common assumption that the transfer between compartments is linear although plasma concentration-coupled tissue clearance (K_ep, per min) is exponential decay (1/e^Kep·t) for (small) molecules that are renally-cleared and less toxic. The common relationship for the DCE-MRI pharmacokinetic models are based on the earlier Crone indicator dilution method [106] in which tissue extraction (E, 1 – e^−P*SA/F; Appendix I) is related to experimentally-determined tissue capillary permeability-surface area product (P*S.A., a.k.a. K_trans) normalized to tissue blood flow (F); tissue extraction is also related to clearance (efflux), e^−Kep/F.

There is also a model relationship for the non-linear (integrated) increase in tissue volume of distribution (V_D’) over the baseline (V_D, fractional y-intercept) over experimental efflux time (minutes) as the forward transfer rate per min (K_trans, min⁻¹) in model selection (Model 1, 2 or 3; [107]). The generalized kinetic model (GKM) is based on Model 3 as the three-parameter model with outward efflux (K_trans) inoto extracellular tissue space (EES, V_D) blood plasma inward rate constant (K_ep, K_b) on the Patlak x-axis plot; the y- intercept V_D parameter is sometimes required for the model [107]; and there is a strong correlation between the ¹⁴C-AIB QAR K_i forward transfer constant (K_trans, K_i) and the Gd-DTPA DCE-MRI GKM model K_trans parameter [108]. DCE-MRI-based bi-compartmental modeling is in T₁-weighted concentration space (mM) with a linearized signal intensity (SI) to DTPA/DOTA (MW 404 Da)-chelated Gadolinium (Gd, MW 157 Da) concentration determination by the product of the molar relaxivity (r₁⁻¹), the longitudinal relaxivity (R₁), and dynamic imaging SI/SI₀ ratio of the low T_R/T_E dual -flip angle (12^O, 3^O) FFE sequence-based imaging [64]; and the effective blood-brain/tumor barrier tumor interstitium permeability limit to macromolecular therapeutics is 12 nm as determined by serial DCE-MRI of Gd-DTPA-G1 through -G8 PAMAM dendrimer generations (2–14 nm).

Several studies have compared the GKM kinetic modeling parameters for the detection of the degree of hyperpermeability across tumor types to tumor tissue histopathology, and show high correlation between K_trans or V_e and lesion grade (r, 0.72–0.78) along high probability for dissimilarity with high discriminatory AUC between Grade II and Grade IV lesions (Sens: 100%, Spec: 93.3%) [109]. Other MRI-based parameters include the blood oxygenation level dependent (BOLD) susceptibility weighted imaging (GE SWI) gradient-echo (GE) Δ R₂*plateau tumor signal parameter in the presence of tortuous vascular density (V_D) [110], which shows better correlation with microvascular vascular density (MVD, 5–10 μm vessels) and vascular density than the GKM model-derived parameter, and also sensitivity and specificity for distinguishing molecular gene expression marker differences in high-grade glioma (HGG) [111] and could be applicable for other tumor types (i.e. exposure-related). Thus, there is applicability of pharmacokinetic model-based parameter determination for differentiation of earlier grade and higher grade lesions within the range for lower permeability brain neoplasms with DCE imaging (Appendix I), and also could have positive predictive value (PPV) for early detection of non-nervous system peripheral solid tumors in worker populations with comorbid risk factors and higher probability for malignancy.

These further developed models are also applicable for the study of circulatory effects of experimental high-molecular weight, density and size or aspected-size therapeutic agents with an increased probability for risk due to non-selectively and extended half-life-related systemic toxicity. Thus, since bioengineered particulate matter toxicity occurs to tissue capillary walls, and the lining endothelium with secondary alterations to the endothelium epicalyx/glycocalyx layer (EGL) and the sub-endothelium basement membrane collagen subunits, which are endothelial cell-secreted deposition, these methods can be utilized for initial and serial determinations of toxicity-related alterations at in vitro, in situ and in vivo temporal resolution.

6. Geologic detritus particulates

6.1 Inosilicates

Asbestos is the double-chain silica tetrahedral-based inosilicate with sharing of two or three atoms each and is comprised of aspected particulate fibers called amphibole fibers with: i) Non-serpentine subtypes Actinolite, Grunerite, Anthophyllite, Crocidolite and Tremolite (ρ = 2.58–2.83 g·mL⁻¹) (Table 1), these being the primary and law regulated asbestos fiber subtypes; and ii) the serpentine asbestos form is Chrysotile (ρ = 2.53 g·mL⁻¹) within the density interval of the silicates 2.196 g·mL⁻¹ (amorphous) - 2.648 g·mL⁻¹ (α-quartz). The amphibole asbestos subtypes are defined short fiber (SAF) of less than 5 μm in length, and the long fiber (LAF) asbestos ≥5 μm based on study of Crocidolite, Tremolite, Dawsonite and Wollastonite [31].

Asbestosis disease/mesothelioma, and related lung carcinoma result is an increase in mortality (%) based on an early epidemiologic cohort study of disease-prevalence associated mortality (1950s–70s) in which 17,800 U.S. and Canada trade workers (i.e. welders, shipfitters) were the 10- year prospective cohort for recording of Asbestosis onset radiographic findings in asbestos insulators [32]; i) mesothelioma is associated with a significant increase in mortality over expected in persons with plaques as compared to controls (Ref. [32] Table 8 (df = 6), post-hoc X²p-value (Pr_sig), 0.001; Appendix II. Post-analyses: Chi Square; Table 6, under respective table), and there is also a low probability for lung carcinoma-associated increase in mortality that could be significant (Ref. [32] Table 8, X²p-value, 0.15, α = 0.05); ii) there is an increase in lowest grade abnormalities on X-ray over expected with asbestosis exposure in the 0–9 year and 10–19 year duration of contact Ref. [32] Table 2 (df = 4), post-hoc X²p-values, 0.002, 1.65E-08; Table 4), and the 40+ duration of exposure groups post-analysis confirms an decrease in observed Grade I asbestosis X-ray abnormalities with an increase in Grade 2 asbestosis-like abnormalities over expected; Table 4, post-hoc X²p-value, 7.455E-12); and iii) there is shift from fewer observed abnormal X-ray findings to normal in-between 1956 and 1960 and 1961 Ref. [32] Table 10 (df = 3), post-hoc X²p-value, 0.0055; Table 7). An increase in mortality percent (%; Ref. [32] Table 5; data not post-hoc analysed) from lung (bronchogenic) carcinoma is maximal between the 25–29 and 35–39 years of employment exposure strata, and the increase from mesothelioma between the 30–34 and 40–44 years of the same with the skew of the distribution, and the majority in Shipwrights and Boilermakers. The higher mortality from pleural mesothelioma is in the 35–39 year exposure interval, and the higher proportion of peritoneal mesothelioma is over a 45+ period due to metastatic pleural disease. Thus, asbestos exposure results in disease with latency, increased bronchogenic carcinoma earlier and mesothelioma later, in addition to there being the non-linear increase in risk of lung carcinoma with cigarette smoke co-exposure in workers with asbestosis as reported by the group.

There is an increase in lung tumor and mesothelioma with exposure to >8 μm length particles that have narrower diameters with the highest correlation to tumor for particles with a > 32- to 16- fold aspect ratio, i.e. in length: width strata, > 8 μm ≤ 0.25 μm (r = 0.80), and > 4–8 μm ≤ 0.25 μm (r = 0.63) [31]; and there is the particle aspect-tumor initiation relationship after deposition by inhalation and trans-mesothelial peritoneal metastases, or after direct intraperitoneal inoculation in animal models. The other determinant of toxicity appears to be atomic constituency, for example, of some more recently identified asbestos forms (IARC, 2012; [30]), Winchite [(Ca²⁺, Na¹⁺) Mg²⁺₄ (Al³⁺, Fe³⁺, Mn²⁺) (Si₈O₂₂) (OH̄)₂] and Richterite [Na¹⁺₂, Ca²⁺) Mg²⁺₅ (Si₈O₂₂)(OH̄)₂], in addition to Crocidolite [Na¹⁺_2, Fe²⁺_3, Fe³⁺_2, Si₈O₂₂ (OH̄)₂] containing Riebeckite [Na¹⁺₂Si₈O₂₂(OH̄)₂] as the classic common asbestos type as an example of ionic ferrous and/or ferric iron in a polyhedral lattice of silicon oxide. In addition to the presence of covalently bond SiO₂ lattice in asbestos fibers, these are comprised of intermolecular ionic bond element interactions (Fe²⁺, Fe³⁺), and ferruginous bodies marked by Perl’s oxidized iron stain (Fe³⁺) within tissue interstitium has also been studied in a cumulative exposure cross-sectional study of synchrotron-based x-ray radio-opacity imaging of asbestos bodies with associated Ferritin (variable size), and X-ray fluorescence microscopy (μ-XRF) for elemental composition analysis, which are intracellularly localizing in macrophage-type cells and also present extracellularly (interstitial); and the asbestos body fluorescence analysis reveals high amounts of K > Mg > Na > Fe, and energy absorption analysis (micro-XANES) reveals presence of ferruginous asbestos fiber body constituents, ferrihydrite-containing protein Ferritin (61.7%), and Hematite (22.1%) and Crocidolite (16.2%) [33].

Also, the U.S. Geological Survey asbestos fiber mixtures have now been characterized by aerodynamic equivalent diameter (D_ae), equivalent diameter (D_eq) by TEM, and settling velocity (V_t) measurements at elutriator flow funnel settings to capture asbestos fibers ≤2.5 microns, and K-factor atomic number (Z)-corrected element composition analysis [36]. Thirteen percent (%) of the mixture is respirable fraction fibers by elutriator recovery at expected respiratory flow (see Appendix I, RPM sampling efficiency); and by STEM mode energy-dispersive x-ray (EDX) spectrum analysis, elemental composition correlations are determined [36]: Fe to Ca or Mg, and Ca to Na ratios are negatively correlated for co-presence, while Mg and Ca, Fe and Na (r = 0.573), and K and Na, show positive correlations. The asbestos particle aspect and composition toxicity results in different histopathological subtypes of mesotheliomas includes a breadth of transformed cell types including fusiform (fibrogenic, osteogenic, giant cells) and pleiomorph (medullar, tubulopapillar).

6.2 Silica and inosilicates

The least soluble particulates include SiO₂ and TiO₂, and aluminum (III) oxide (Al₂O₃) is less, and CuO and non-amorphous carbon black (Printex 90) are least, gene expression effects of which have been correlated with solubility; the least soluble result in greater magnitude gene expression effects upon internalization with maintained positive correlation for presence of tissue neutrophils, Saa-1 (liver) and Saa-3 (lung) mRNA [112]. In addition to gene and protein expression responses to bioengineered particle exposure, differences in gene expression between inosilicates, rutile Cristobalite crystalline silica (non-quartz) and amorphous silica concentrations of crystalline silica and Crocidolite asbestos, are studied in comparative cell lines, primary (normal) human bronchial epithelial cell (NHBE) and BEAS 2B on the SV40 virus-integrated epithelial cell line by cDNA hybridization microarray of larger sets of genes and/or qRT-PCR of certain gene sets [34, 38].

The effects of mined Vermiculite deposits (Libby amphibole, LA) composed of a mixture of Winchite (83%), Richterite (11%) and Tremolite (6%) asbestos with TEM-based length: width on primary human airway epithelial cells (HAECs) are studied with qRT-PCR measurement of inflammatory marker gene expression in normalized dose-response effect (lL-8, IL-6, COX-2, TNF) [35]; and the geometric mean (μ) lengths of the fiber sets are: RTI Amosite, 10.4 μm (n = 359), LA2000, 3.34 μm (n = 433, width > 1), LA2007 2.47 μm (n = 268) and UICC Amosite, 2.07 μm (n = 222) [35]. There is a higher potency effect and increase in chemokine IL-8 mRNA (CXCL8) levels in response to RTI Amosite particle number dose/cm² cell for equivalent concentrations of ionizable iron (inorganic) and chelatable iron (Fe³⁺) present in both RTI Amosite and UICC Amosite samples as can be determined by ICP-optical emission spectroscopy. The difference in the inflammation-mediated toxicity potential of the fiber sets is due to the prolonged residence time of aspected iron-containing asbestos fiber types inaddition to the probability of the adsorption of oxidized iron forms.

There is methodological validity in same group studies. The findings of two comparative studies with normal human bronchial epithelial cell (NHBE)- and BEAS-2B immortalized normal epithelial cell- types exposed to low- or high-dose crystalline silica, and at 15 and 75 × 10⁶ μm²/cm², or low dose inosilicate (iron-containing asbestos). There are no cell viability differences for low- or high-dose silica-exposed BEAS 2B cells, however there are differences in gene expression by cDNA microarray with shift towards FOS (cFOS) and IL-6 related gene expression with Cristobalite silica exposure (p < 0.05/cut off ≥2.0-fold) with reduced false positive risk (FDR 5%) [38], see Table 1. The rank order of gene changes between control and respective exposure comparisons is high dose Cristobalite > low dose > Amorphous with little difference in cell adhesion pathway gene activation between the two silica types by gene ontology (GO) molecular pathways correlation analysis; and there are differences in gene expression between exposure between normal and virally-transformed cell types. Low dose Cristobalite silica results in minimal differential gene expression in NHBE cells (3 genes; Table 1) [34]; the Asbestos exposure pathway is more towards JUN, IL-8 and MMP-1 metalloprotease gene expression by qPCR for specific gene expression, and common silica and asbestos pathway activation includes glucocorticoid (GR), whereas divergent pathways are extracellular matrix synthesis proteoglycan with Crocidolite asbestos exposure, and the Aryl hydrocarbon receptor (AHR) pathway with crystalline silica exposure.

6.3 Lead

Lead is found in steel at 0.15–0.35% concentration in addition to carbon, and is released during the abrasive blasting process, and poses a risk, as there is little positive or negative correlation and independence to air flow velocity and there is an increase in exposure-related Lead concentration in blood in both blaster and vacuumer [113]. There is the effect of exposure during the ore lead extraction smelting process and to combusted lead particulates over prolonged duration during which dissolution favors ionic Lead (Pb²⁺) release. There is an increase in the stratified years interval SMR (1–5 yr., 5–20 yr., 20+ yr) for renal carcinoma and non-malignant respiratory disease (emphysema, pneumoconiosis) [46]: this is in employees with BLL at 56.3 μg/dL (2.73 μM) and 0.366 ppm average airborne Lead concentration exposure (3.1 mg/m³; Table 3, Appendix III. Particulates toxicity industrial hygiene), but is with low probability for anemia (10%), in a low co-exposure to Arsenic (0.0133 ppm) and Cadmium (0.0016 ppm) population of workers with unknown smoking history. Inaddition to accumulation levels in long bone parts (i.e. tibia, 99.4 μM), there is a negative x- (patella Pb, μg/g), y- (LINE-1 methylation) variable regression relationship for increasing Lead levels to 40 μg/g bone concentration (0.194 mM) that is consistent with decreased inactive LINE-1 DNA and RT DNA methylation with increased Pb concentration; this evidence comes from a cross-sectional study (2010) of global DNA CpG island methylation of retrotransposon viral origin human DNA integrated long repetitive base sequence elements (LINE-1) and reverse transcriptase (RT)-encoding elements [114].

There are also increases in inflammatory marker expression (IL-6) inaddition to of both vascular permeability factor gene (VEGFA) and decoy receptor (sVegfr1) gene expression as per another more recent single timepoint study (2017) enrolled Lead-Zinc workers with elevated BLL (37 μg/dL) and ZPP levels as compared to controls with unknown alcohol use history [47]. Additional support for the Lead transition metal-type toxicity effect comes from cell culture exposure with applied Lead Acetate (100 nM – 1 μM; Table 1) on transiently-transfected chondrocytes with measured reporter luciferase (LUC) activity of AP-1 and Nf-KB plasmids in response to Pb(C₂H₃O₂)₂ alone or with applied PTHrP [50], in which its application alone results in a decrease in cell survival factor gene Nf-KB without change in AP-1 factor subunit genes (FOS, JUN) activity, but an increase in AP-1 activity with PTHrP as effect modifier. The effect of released ionic Pb is condition-dependent and can in certain instances shift the cell response towards pro-angiogenic progression. The effect of released ionic Lead (Pb²⁺) is co-exposure condition-dependent, and can in certain cell types and instances shift the cell response towards pro-angiogenic phenotype, inaddition to its known negative effects on neuronal cell growth in association with Casp8 gene promoter hypomethylation (CpG) [48] and over activation resultant neurotoxicity.

6.4 Manganese

Manganese has paramagnetic properties, and is present in various oxidation states, Mn²⁺ (Mn(OH)₂, basic), Mn³⁺ (Mn₂O₃, Mn(OH)₃) or Mn⁴⁺ (MnO₂) in the presence of H₂O₂ reactive Oxygen species (ROS) with co-product hydroxides formed (OH⁻, OH·) as per the Pourbaix electrical potential to pH relationship [115]. It is reactive with Transferrin as Mn³⁺ in the presence of base (HCO₃⁻) similar to that of ferric iron, but with less affinity than ionic Chromium (Cr³⁺) due to a greater ratio of bicarbonate to metal as determined by optical absorbance [116], and as shown by the Cr (OH)_x-Transferrin peak on EPR spectroscopy [116]. An example of exposure to particulate matter metal mixture is in welding with fume generation, and dissimilar solid or flux-cored pre-weld composition-containing wire-dependent secondary aerosolizing particulates of differing solubilities and toxicity.

These hazardous by-products of welding have been characterized [117]: i) the size distribution of particles generated is between 3 to 180 nm (SEM, TEM); ii) the mixture in a) solid wire gas metal arc welding (GMAW; S₁, S₂) is a combination of oxides, Fe_xO_y, Mn_xO_y, CrO₄²⁻ (Cr^VI), Cr₂O₃ (Cr^III/IV), SiO₂ and BiO₂, and in b) flux-cored wire arc welding (FCAW; F₁, F₂), this includes the additional composition of ionically-bonded Alkalis (Na⁺, K⁺; Group 1, Period 3, 4) and Halogen (F⁻; Group 17, Period 2) elements (XPS); and iii) the PBS soluble metal is a small proportion in solid wire welding, while in flux wire welding, it is most of the (by-) product comprised of Mn, Cr^III and Cr^VI, predominantly CrO₄²⁻ (Cr^VI). The weld by the FCAW process has flux by-product with increased solubility and the weld proportion of metal product content improved in purity with less metal oxide, but there is a large proportion percentage (%) with higher Pr for sig. For concentration-dependent cytotoxicity at 1 day and genotoxicity as early as 3 hrs at 10⁻⁴ M concentration in the flux FCAW F₁ and F₂ process type welding [117]. Thus, any combustion by-product composition has Manganese in its ionic (Mn²⁺) form, which is the energetically-favorable form at pH < 8, in both sub-type processes (S₂, F₂), which is supported by the experimental data showing solubility decreases with addition of increasing concentration of Fluorine (F⁻).

As to the Manganese effects on genomics, DNA re-sequencing results shows Manganese efflux transporter gene SLC30A10 and SlC39A14 polymorphism ([118]; Table 1), and changes result in other related gene expression such as RBFOX1 [119], TXN8 [48] and MPP+ responsive influx transporter gene DMT1 [120], for example bisulfite Cytosine (− > Uridine) substitution DNA epigenomics shows methylation of the splicing regulator gene RBFOX1 (A2BP1) at position cg02042823, which has been studied by qT-PCR and RNA-seq transcriptomics. The double-allele mutant loxed gene (RBFOX1^−/−) murines show a decrease in exon inclusion for certain channel genes such GABRG2 and GRIN1 [119] with the net effect being lower stimulus intensity thresholds required to evoke EPSPs and less inhibitory ion flux as per potential recordings showing this effect.

In addition to study of alterations in gene methylation of the RBFOX1 gene, study of cell morphology shows altered gender-dimorphic dendritic spine count with spatial frequency lower in females (10 μm⁻¹), and higher in males, after subcutaneous exposure to repeated dosing of ionic Manganese as MnCl₂ [121] such that this would result in Dopaminergic with a sex-specific dimorphic effect on neurite bleb budding in association with dendrite density. Similar local concentrations achieved in the striatum in both and the inverse inhalation effect of Maghemite (Fe₂O₃) in the Welder workers [122] with the clinical sign of cocked-gait as sign of basal ganglia and midbrain nuclear group toxicity with internal capsule upper motor neuron disinhibition effects due to exposure intensity toxicity early in Manganism, and then exposure duration-related toxicity in Manganese-induced Parkinsonism).

The experimental data on the transfected overexpression of the divalent influx transporter gene, DMT1, that transports Mn²⁺ present at increased local concentration in SH-SY5Y pre-differentiated neuroblastoma neuronal cell line show the effects of Manganese (Mn²⁺) in comparison to ferrous Iron (Fe²⁺) with more of an initial shift in cell compliance (P_eff C, a.u.) to the phosphorylation of the c-JUN AP1 transcription factor terminal kinases [123], and towards the resultant p53-mediated R-shift for activation of the Bax-, Bad- and Bim- apoptosis cascade, and/or DNA repair and cell cycle changes. For Manganese, i) the negative electrical potential and neutral pH favors its ionic and soluble form; and ii) it is the presence of efflux channel and certain cell surface internalization receptors that results in its initial effect followed by its later apoptosis-related toxicity to basal ganglia cell types.

6.5 Copper ore arsenate

Either Arsenic or Stibium, Group 15 (semimetals), are bound to Sulfur (Group 16, nonmetal), and there is 91% dissolution of Tennantite at high temperature (212° F) over a 2 hour period in basic electrolyte solution (NaOH) containing Na₂S [124]. The mined copper ore contains transition metals, Copper, Arsenic and Iron in polyhedral/hextetrahedral configuration with some forms containing ionic free forms of Iron (Fe^3+/2+) and Zinc (Zn²⁺) (Table 1). Copper in oxidation states 1⁺ or 2⁺ results in interactions with metallothioneins with overload effect on DNA, resulting in conformational changes [125]. Certain studies show the effect of ionic Copper in form of Cu (II) acetate ((CH₃)₂COO-)₂ on cells that when applied results in overexpression of the NFκB as per plasmid transfectant fluorescence and of pathway and related genes as determined by qRT-PCR and confirmed by siRNA data [43]. Copper (Cupric) exposure to cells (i.e. Hepatoma G2) results in a time-dependent increase in NFκB2 target gene CXCL8 (IL-8) and IER3 gene expression, inaddition to overexpression of NFE2L2 (NRF-2) and Phase II detoxification pathway genes, HMOX1 (HO-1) and GCLC. In desaturation conditions (Section IV), the dissolution kinetics favor conversion of As₂S₃ to H₃ASO₃ and hydrogen sulfide gas (H₂S); Arsenate (AsO₄)³⁻ and Arsenite (AsO₃)³⁻ in respective trivalent and pentavalent oxidation states are slow dissolution molecules with the potential to transform cells in low-chronic exposure conditions with ROS pathway and related gene activation [126], and hypoxia-associated genes (i.e. HIF-1α) in transformed cells [44]. In the Chile Copper ore sub-regions (various forms, see Table 1) with mining area-specific differences in exposure risk [127], there is increased gender-specific mortality in a low-smoke exposure prevalence population of men and women ≥30 years of age as analysis by the age-weighted standard mortality ratio (SMR; [14]) [45] supports increased mortality due to carcinomas of the bladder (SMR 6.0, M; 8.2, F), kidney (SMR, 1.6, M; 2.7, F), pulmonary (SMR, 3.8, M; 3.1) and dermatologic (SMR, 7.7, M; 3.2, F), and the population attributable risk (PAR) in that mining region is 9.7% (M) and 4.9% (F) for ore exposure-related carcinoma. Leaching into drinking water reservoirs with peak exposure concentration at 870 ug/dL (116 μM) results in an increase in mortality due to lung cancer is attributable to increased ore particulates air concentration. The presence of additional transitional metals, Iron, Stibium and Zinc present in Copper ore particulates could result in effect modification.

7. Exposure prevention and surveillance guidelines

In the various sectors there are increased levels of process-generated smoke particulates or aerosol mists and related hazards that require the implementation of the hierarchy of controls and access to online information repositories [128]. Surveillance of occupationally-associated promontory habits prevalence has shown co-existence of comorbidity such as in the asbestos abatement workers [129]. Chest X-ray radio-opacities are present in individuals not exposed to dusts in an intercontinental population with gender- and age- specific differences with low prevalence at ≤50 yoa, in females (F, 0.4%) or blue collar workers working in non-exposure conditions (0.21–0.25%) [130]. Exposure surveillance comprises spirometry and CXR for monitoring of both PMF and simple CWP (under MSHA (30 USC 801–962) with a PEL of 2 mg/m³ (REL 1 mg/m³) or reduced PEL for silica content (30 CFR 70.101, see Table 4, Appendix III), and the requirement of a Black Lung (B) read (42 CFR Part 37) as per ILO classification for radiographs (1980) [131], Table 1. In the particulates exposure-monitored individuals with FEV₁/FVC ratio increases due to silica or asbestos exposure, there is a higher probability for parenchymal abnormality by age and Laborer/Cleaners class (Model I), but not in an un-conditional Model (II) without inclusion of asbestos exposure in logit probability [132].

Surveillance measures can be proposed for higher risk work groups in similar exposure conditions, and at the action limit there is the requirement for substance-specific NIOSH-approved respiratory protective device use under the OSHA Respiratory Protection Standard (29 CFR 1910.134). There is environmental risk for carcinogenesis with air exposure concentration at 8.40x10⁻² (Cr^VI) and majority of years of life lost due to water exposure to Free Cr⁶⁺ (2.09x10⁻⁵; YLL_water, 92%) [133] [REF] with the effective cancer risk at two-orders higher concentration for the particulate, aerosol and water partitioned agents tested (i.e. Hg^II, Cd^II; Lindane, DDT). There is increased risk for lung carcinoma in certain worker sub-groups such as Crane, Derrick and Hoist workers (OR_adj 14.4; 3.36% PAR), and with ≥10 years exposure to Coal Dust (IARC 1) adjusted for other variables shows greater odds of lung cancer in cases over non-cancer (OR_adj 2.0) and cancer (OR_adj 1.5) control group populations [134], as per a 1992 two-control group case-control study applying multi-coefficient variable probability regression (unconditional) modeling; as per the study, any duration of cigarette smoke exposure ≥20/day results in increased risk of lung carcinoma (OR_adj 2.1) in Asbestos–exposed persons.

Area sampling of air for aerosolized viruses transmitted by speak, cough or sneeze [54] and particulates is by general elutriation devices [16], carbon-filled adsorption capillary tubes for lipophilic gaseous vapors, or personal dosimeter devices with pore size threshold filters (Table 5, Appendix III), to adhere to certain exposure threshold monitoring requirements at the action level (AL) to begin biomonitoring. The OSHA air concentration medical action level for Lead (Pb^II, Pb²⁺, IARC 2A; MW = 206.2 Da) is 0.03 mg/m³ (10⁻¹⁰ M) with a PEL (ceiling limit) of 50 ug/m³ under 29 CFR 1910.1025 (https://www.osha.gov/laws-regs), and Manganese (Mn^II) has a threshold limit value (TLV, respiratory limit)-8 hr. TWA of 0.02 mg/m³ as recommended by the American Conference of Governmental Industrial Hygienists (ACGIH) [15]. Occupational surveillance of inhalable dust exposure includes for IARC Group I carcinogens, Asbestos (29 CFR 1910.1001, Industry), Silica (29 CFR 1926.1153, Construction), Chromium (CrO₃, Cr^VI; 1910.1026, GI), and also Arsenic (Ar^V, inorganic; 29 CFR 1910.1018) for which there exists a permissible exposure limit (PEL) of 10 ug/m³ with an AL that is one-half of this limit.

Traditional X-ray diffraction has sufficient sensitivity for detection of high-Hounsfield unit pathology in linear scale with exposure monitoring reads as per the ILO reads classification system [131], and has high specificity to detect early metastatic foci (91%), but low sensitivity (41%) [135]. Non-contrast chest CT can be utilized for study temporal tracking of pulmonary lesions at high-resolution as it has improved sensitivity (74%). T₁-weighted MRI at early time point after injection early contrast enhancement (ECE; 4.5 min) can be utilized for signal intensity (SI)-based hyper-permeability detection of mesothelioma foci by region of interest (ROI) as it has high sensitivity (91%) and 15% false positive rate with a high-correlation κ statistic (0.77) [136]; and ¹⁸FDG PET has utility for quantitative detection of solid tumor tissue hypermetabolism and metastatic disease with a high true positive rate (92%) and high specificity (93%) [135]. The diagnostic determination of occupationally-related neoplasia can include the application of macromolecular contrast enhancement agents for sensitive and specific detection of the hyperpermeable pathology of solid tumors on CT and on MRI during surveillance or treatment course.

8. Conclusions

There are several mineral ores containing transitional atoms with polyhedral bond structure; there is the utilization of minerals of mined mineral ores in the various biotechnology sectors; there is the development of bioengineered nanoparticles for enhanced accumulation and retention effects; there is the bioaccumulation of human-made synthetic materials including plastic particulates with very slow dissolution rates and potential for particulate matter environmental toxicity that alter the normal cell molecular mechanisms.

Certain permissible threshold limits are established by the U.S. regulatory agencies for minimizing environmental or occupational exposure to individual mineral constituents measured by mass spectroscopy or by fluoroscopy or to whole particulate matter. Causality for certain mineral exposure-associated toxicity and disease is established by the combined evaluation of in vitro test results, animal study experimental data and human epidemiologic findings by probability of significance. Methods that are applied in study design include exposure air sampling by elutriator air flow, characterization of particulate dimensions and shapes by electron microscopy or particle light scattering, measurement of particulate matter dissolution, and biomonitoring of biochemical metabolites in workplace surveillance, gene expression level alterations by cDNA microarray or by RNA sequencing with pre-amplification by qPCR, small animal subject and human blood plasma and tissue imaging with pharmacokinetic modeling, descriptive statistics and epidemiologic -based risk delineation by alternative hypothesis and p-value thresholds.

The reasons for particulate matter toxicity can be understood with knowledge of the diversity of topics covered in this chapter on: i) aerosol and particulate matter classes, ii) natural and bioengineered particle types, iii) respiratory tree deposition mechanisms, iv) imperfect particle properties and flow regimes, v) solvation variables, vi) compartmental modeling parameters, and vii) workplace exposure limits and guidelines. The material covered in this chapter will be pertinent to the determination of causal and synergistic relationships for new toxicants and mixtures, and industrial, mining and construction sector exposure air sampling, high-resolution tissue bioimaging and monitoring of general and specific external exposomic effects on DNA and polymorphism and for the further understanding of mechanisms of particulate matter toxicity, and disease initiating and promoting variables.

Conflict of interest

None.

Declarations and acknowledgements

The writing of this chapter is supported by the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health with also feedback on microscopy methods by Richard Leapman and colleagues (Maria Aronova), and the Department of Environmental Medicine and Public Health, Mount Sinai School of Medicine, USA with feedback in the system by John Meyer on respiratory particulates, and James Godbold in Biostatistics on post-analyses of Ref. 31 study data. Additional feedback was provided by Behzad Ghanbarian on solution percolation, Mark Goldberg and Jack Caravanos on the air sampling and industrial hygiene, Phil Landrigan on the initial Lead smelter study and exposure thresholds, Roberto Lucchini on the effects of Manganese exposure-related toxicity to susceptible populations, Paul Tofts on the vascular plasma term modification in the general kinetic model, Charles Michel on the convective effects of macromolecules, and Gunter Oberdorster on the inhalation toxicology aspects. All previous co-authors including John Butman, Steve Fung, Ariel Kanevsky, Colin Wilson, Haitao Wu, Alioscka Sousa and Matthew Hall have reviewed the pertinent contents.

A.Appendices

A.1 Microvascular fluid exchange and pharmacokinetic modeling

Unidirectional three-parameter transcapillary permeability model issue extraction by indicator dilution

$$E_{\mathrm{F}}=1-\frac{1}{e^{\frac{P·S}{\mathit{CBF}}}}$$E1

where E_F, brain tissue indicator extraction fraction, a.u.

P, permeability, x10⁺¹ mm/sec

S, surface area, x10⁺² mm²

Q, blood flow (i.e. CBF), microvascular capillary flow per unit mass of tissue per min, ml/60 sec · g tissue, mm³/sec

if P·S product - > ∞, then E_f ≈ 1 and all of the indicator is extracted from blood plasma into the EES and is a small molecule permeable across the blood-CSF barrier and the blood-brain barrier and is a barrier permeability indicator

if P·S product - > 0, then E_f ≈ 0 and none of the indicator is extracted and the indicator is a macromolecular contrast agent and blood volume indicator

Bidirectional flux two-parameter transcapillary permeability model for hydraulic conductivity with Peclet variable for capillary connective flow

$$\frac{J_{\mathrm{V}}}{A}=\mathit{Lp}\left[\mathit{\Delta P}-{\sigma }^2{\pi }_c\frac{\left(1-\frac{1}{e^{\mathit{Pe}}}\right)}{\left(1-\sigma \frac{1}{e^{\mathit{Pe}}}\right)}\right]$$E2

where J_v, effective hydraulic flux into tissue in 10⁻⁶ cm per sec

A, surface area for transcapillary exchange in cm-squared

L_p, hydraulic conductivity coefficient of water permeability in cm per sec · cm H₂O

σ, reflection coefficient to transcapillary macromolecule transfer across capillary wall, a.u.

π_c, capillary osmotic pressure in mm Hg or cm H₂O

P_e, Peclet variable ratio for macromolecule convective flow through capillary, a.u.

Bidirectional three-parameter generalized kinetic model (GKM) pharmacodynamic model with vascular plasma volume adjustment term (as per section Refs.)

$$K\text{trans}=\frac{C_t\left(t\right)-V_p\ast C_t\left(t\right)}{{\int }_{\mathrm{Ʈ}}^t\frac{C_p\left(t\right)}{e^{\mathit{Kep}\left(t-\mathrm{Ʈ}\right)}}\mathit{dt}}$$E3

where C_t (t), concentration in tissue at time t in minutes

C_p (t), concentration in plasma at time t

Ʈ, imaging time at initial sequence contrast enhancement

V_p, plasma vascular volume

V_e, extracellular extravascular volume, tissue interstitium volume

K_trans, K_i, positive slope forward (outward) transfer rate constant per minute (or apparent tissue V_D (V_D’) per minute)

K_ep, K_b, negative slope reverse (inward) transfer rate per minute

K_trans range: indication.

Upper tier: high-hyperpermeability lesion, i.e. inflammation

Mid tier: mid-hyperpermeability lesion, i.e. high grade tumor

Lower tier: low-hyperpermeability lesion, i.e. low grade tumor

A.2 Chi Square post-analyses

	Exposure duration (yrs)
X-ray Grade (of abnormality)	0–9	10–19	20–29	30–39	40+
Asbestosis Grade 1	Exp, 24; (Obs, 36)	Exp, 113; (Obs, 158)	Exp, 38; (Obs, 35)	Exp, 114; (Obs, 102)	Exp, 77; (Obs, 35)
Asbestosis Grade 2	Exp, 8; (Obs, 0)	Exp, 39; (Obs, 9)	Exp, 13; (Obs, 17)	Exp, 39; (Obs, 49*)	Exp, 27; (Obs, 51)
Asbestosis Grade 3	Exp, 3; (Obs, 0)	Exp, 15; (Obs, 0)	Exp, 5; (Obs, 4)	Exp, 16; (Obs, 18)	Exp, 11; (Obs, 28)
Chi Test; p-value	0.00017	5.507E-13	0.4294	0.1313*	6.303E-17

Table 3.

Table 2 post-analysis of grade 1, grade 2 and grade 3 X-ray abnormalities (of ref. [32]).

*

trend for difference between groups (Grade 1, −2 and − 3).

	Exposure duration (yrs)
X-ray Grade (of abnormality)	0–9	10–19	20–29	30–39	40+
Asbestosis Grade 1	Exp, 27; (Obs, 36)	Exp, 124; (Obs, 158)	Exp, 39; (Obs, 35)	Exp, 112; (Obs, 102)	Exp, 64; (Obs, 35)
Asbestosis Grade 2	Exp, 9; (Obs, 0)	Exp, 43; (Obs, 9)	Exp, 13; (Obs, 17)	Exp, 39; (Obs, 49)	Exp, 22; (Obs, 51)
Chi Test; p-value	0.002	1.649E-08	0.5043	0.1565	7.455E-12

Table 4.

Table 1 (a) post-analysis of grade 1 and grade 2 X-ray abnormalities (of ref. [32]).

	Exposure duration (yrs)
X-ray Grade (of abnormality)	0–9	10–19	20–29	30–39	40+
Asbestosis Grade 2	Exp, 0; (Obs, 0)	Exp, 6; (Obs, 9)	Exp, 15; (Obs, 17)	Exp, 112; (Obs, 102)	Exp, 64; (Obs, 35)
Asbestosis Grade 3	Exp, 0; (Obs, 0)	Exp, 3; (Obs, 0)	Exp, 6; (Obs, 4)	Exp, 39; (Obs, 49)	Exp, 22; (Obs, 51)
Chi Test; p-value	Und.	0.1677	0.6361	0.9615	0.5655

Table 5.

Table 1 (a) post-analysis of grade 2 and grade 3 X-ray abnormalities (of ref. [32]).

	Exposure duration (yrs)
	Lung cancer	Mesothelioma	G.I. cancer	All other cancer	Ischemic heart disease	Chronic bronchitis	All other causes
Plaques	Exp, 15; (Obs, 19)	Exp, 15; (Obs, 23)	Exp, 7; (Obs, 7)	Exp, 13; (Obs, 7)	Exp, 40; (Obs, 34)	Exp, 11; (Obs, 9)	Exp, 27; (Obs, 22)
Controls	Exp, 8; (Obs, 4)	Exp, 8; (Obs, 0)	Exp, 4; (Obs, 4)	Exp, 7; (Obs, 7)	Exp, 23; (Obs, 29)	Exp, 7; (Obs, 9)	Exp, 16; (Obs, 21)
Chi Test; p-value	0.1548	0.0012	0.9995	0.9859	0.3166	0.5104	0.263

Table 6.

Table 8 post-analysis of X-ray asbestosis contact-related pleural plaques in exposed and non-exposed groups (as per ref. [32]).

	Onset of work (interval)
X-ray Grade (of abnormality)	< 1950	1951–1955	1956–1950	> 1961
Normal	Exp, 90; (Obs, 81)	Exp, 107; (Obs, 98)	Exp, 180; (Obs, 174)	Exp, 164; (Obs, 188)
Abnormal	Exp, 76; (Obs, 85)	Exp, 91; (Obs, 100)	Exp, 153; (Obs, 159)	Exp, 139; (Obs, 115)
Chi Test; p-value	0.1702*	0.1935	0.4986	0.0055

Table 7.

Table 10 post-analysis of abnormal and normal X-ray findings for beginning work year intervals (of ref. [32]).

*

trend for difference between groups (Normal, Abnormal).

A.3 Particulates toxicity industrial hygiene (adapted from Ref. [15])

1. Threshold limit value

   $$\mathit{TLV}\mathrm{ppm}=\frac{\left(\mathit{TLV}\right)\left(\mathrm{Air}\mathrm{vol}\right)}{\left(\mathit{MW}\right)};$$E4
   $$\mathit{TLV}\mathrm{ppm}=\frac{\left(\mathit{TLV}\right)\left(\mathrm{Air}\mathrm{vol}\right)}{\left(\mathit{MW}\text{conv}\right)}$$E5

   where 1 ^(a), MW, g/mol, or 1 ^(b), MW (conv., mg/mol), MW x 1000 mg/mol.

   1 ^(a) TLV, g/1 x 10⁶ L, or 1 ^(b) mg/m³

   1 ^(a) Air vol, 24.45 L/mol, or 1 ^(b), 0.02445 m³/mol

2. Time-weighted mean

   $$\mathit{TWA}\mathrm{m}\left[\right]=\frac{\sum \left(n_1\ast \left[\right]+\dots +n_m\ast \left[\right]\right)}{\sum \left(n_1+\dots +n_m\right)}$$E6

   where TWA_m, Time-weighted average concentration.

   [], concentration

   n, hours of exposure at TLV concentration

   m, hours in shift

3. Fraction of TLV exposure

   $$\text{TLVfract}=\sum \left(\frac{{\left[\right]}_1}{{\mathit{TLV}}_1}+\dots +\frac{{\left[\right]}_z}{{\mathit{TLV}}_z}\right)$$E7

   where TLV_fract, overall fraction of threshold limit value exposure (> 1, exposure limit has been exceeded).

   [], concentration

   z, TLV for specific chemicals beginning with first exposure []

4. Sampler efficiency

   1. IPM, Inhalable particle matter size range, 50% cumulative reference level

      $$\text{Finh}\left(D\mathrm{a}\right)=-11.8\left(\mathit{ln}\left(D\mathrm{a}\right)\right)+\mathrm{\beta }0$$E8

      Finh (D_ae), IPM efficiency.

      β₀ = 101.

      (, non-inclusion x variable.

      ], inclusion x variables.

      where particle aerodynamic diameter D_a, (0 μm; 1 μm, 2 μm, 5 μm, 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 100 μm], R² = 0.97.

      where 50% efficiency is at standard particle D_a = 100 μm, and 97% efficiency is at std. particle D_a = 1 μm (actual values)

   2. RPM, Respirable particle matter size range, 100% cumulative reference level

      $$\text{Fresp}\left(D\mathrm{a}\right)=-3.79\left(D\mathrm{a}\right)2+2.84\left(D\mathrm{a}\right)+\mathrm{\beta }0$$E9

      Fresp (D_ae), RPM efficiency (small particle).

      β₀ = 99.4.

      where particle aerodynamic diameter D_a, [0 μm, 1 μm, 2 μm, 3 μm, 4 μm], R² = 0.998

      $$\text{Fresp}\left(D\mathrm{a}\right)=\mathrm{\beta }0e^{-0.648\left(D_a\right)}$$E10

      F_resp (D_ae), RPM efficiency (large particle).

      β₀ = 767.

      where particle aerodynamic diameter D_a, [4 μm, 5 μm, 10 μm, 6 μm, 7 μm, 8 μm, 10 μm], R² = 0.992.

A.4 End of chapter educational objectives exercise – Particulate matter toxicity

1. What are the bioengineered macro-particulate classes for extended release effects (Table 2)?

   • Colloid (1–1 μm),

   • Suspension (> 1 μm)

   • Emulsion (H₂O-in-oil (w/o), oil-in-H₂O (o/w)) ± inclusion

   • Micelle (10–200 nm)

   • Liposome (100–580 nm)

2. How are bioengineered particles categorized (Table 2)?

   • Soft nanoparticles (3^x + 2, x = C-C bond)

   • Hard nanoparticles (n^x + m, x = metal oxide bond)

3. Geological detritus is (Table 1):

   • Transition metal oxide/polyhedral bond structure particulates

4. What are the particle properties for elimination or uptake?

   • Charge/exterior oxidation state (neutral, poly-neutral, anionic, cationic, cationo-neutral)

   • Size (1.65–2.09 nm, renal filtration; 4.75 nm, renal reabsorption)

   • Aspect (w, l)

5. What are the terms and relationship for inhalable particle diameters?

   • Aerodynamic diameter (D_ae)

   • Settling velocity diameter (D_a)

   • Volume equivalent diameter (D_ve)

   • Mass equivalent diameter (D_me)

6. What are the determinants in the inhalable particulate matter flow regime?

   • Effective particle density (ρ_eff^II)

   • Particle density (ρ_p)

   • Dynamic shape factor (χ)

   • Knudsen path length (K_n)

   • Velocity-slip correction parameter (C)

7. What are mechanisms of smelter particle formation?

   • Condensation for collision product molecules

   • Coagulation for particles

8. What are the variables of dissolution?

   • Particle type (hard, soft)

   • Dreiding forcefield energy (D_E)

   • Free energy threshold (G_critical)

   • Surface energy barrier to dissolution (H)

   • Defusion entropy (S_f)

   • Partition coefficient (P)

   • Solution electrolyte saturation (1 - α)

   • Volume per molecule (ω)

   • Temperature (T)

9. What are the variables of percolation?

   • Matrix porosity (φ), pore size (r_m), pore number (r_n)

   • Volumetric water content (θ)

   • Percolation (p)

   • Critical threshold for percolation (p_C)

   • Cross-over threshold (p_x)

   • Power variable (t, q)

   • Molecule size (a)

   • Macro-diffusivity (D)

   • Diffusivity, unbounded solvent (D_∞)

10. What are the epidemiologic/statistical methods?

    • Categorical

      • Relative risk (RR), Odds ratio (OR), Chi Square (χ²; Appendix II), HR: Logistic regression

      • Sensitivity and specificity

    • Numeric

      • Parametric methods, SMR

11. What are the primary variables for water and solute permeability modeling (Appendix I)

    • Starling model, 2-compartment, bidirectional

    • Oncotic pressure, π_c; π_i, π_g

    • Hydrostatic pressure, p_c, p_i

    • Reflection coefficient, σ (a.u.)

    • Hydraulic permeability coefficient, L_p

    • Fluid flux parameter, J_v/A/ΔP

    • Solute flux parameter, J_s/A/ΔC

    • Peclet convection diffusion ratio, P_e (a.u.)

12. What are the primary variables for solute pharmacokinetic modeling (Appendix I)

    • Universal diffusional concentration variables, C_b, C_{t intracellular}

    • QAR model, 2-compartment, uni-directional (integrated/non-linear)

    • Influx parameter, K_i

    • GKM model, 2-compartment, bi-directional (integrated decay)

    • Transfer parameters, K_trans, K_ep

    • Fractional variables, v_p, v_e

13. What are US agency threshold limits or groups for toxic exposure surveillance for respirable size range particulates aerosol (Appendix III, Table 3 footnotes)?

    • Threshold limit value, (shift, TLV)⁻¹

    • Permissible exposure limit (PEL)

    • Recommended exposure limit (REL)

    • Short-term exposure limit (STEL)

    • Action level (AL)

14. What is the industrial hygiene approach?

    • Air sampling/personal dosimetry

      • Particulates, flow rate-calibrated sampling pumps with size-specific filters

      • Vapors, monitoring badges and/or solid sorbent tubes

    • Hierarchy of controls implementation

15. What are the methods for analysis of cell macromolecular changes in response to exposure?

    • Fluorescence absorbance/emission (Abs, Em SI, λ)

    • X-ray diffraction (SI, 2-θ°)

    • FTIR reflectance or transmission spectroscopy (SI, λ⁻¹)

    • Mass/ionization spectroscopy (SI, m/z)

    • ¹H NMR spectroscopy (SI, ppm)

    • TEM: STEM_{2.6E2 e−/nm2}, EFTEM_{10E6 e−/nm2} (SI, σ)

16. Methods for analysis for DNA polymorphism and transcription factor/complex binding, RNA expression, methylation and conformational changes include:

    • DNA segments sequencing (reads mapping, assembly; ATAC, CHiP)

    • RNA-sequencing

    • Guide RNA-sequencing/Cas9-CRISPR

    • qRT-PCR with primers

    • cDNA microarray library with FDR Q-value correction

    • Bisulfite C - > U conversion

    • Circular dichroism (CD) spectroscopy (θ°, λ)

17. What are the determinants of particulate matter cell genomic and epigenomic toxicity?

    • Particulate matter exterior properties aspect ratios

    • Transition metal dissolution rates and exterior properties/oxidation states (cell surface interaction, nuclei acid binding/conformational changes)

    • AhR and related pathway agonism

    • Efflux transporter and related gene expression polymorphisms

    • Gene promoter or body methylation changes

Abbreviations

MS	mass spectroscopy
ICP-MS	Inductively coupled plasma-MS
ICP-OES	ICP-optical emission spectroscopy
EF-TEM	energy filtered-TEM
EEL-TEM	electron energy loss spectroscopy
SANS, SAXS	small angle neutron/x-ray scattering
SEM	scanning emission microscopy
XANES	X-ray absorption near-edge structure (element specific)
XRD, XRF	X-ray diffraction or fluorescence
DSL	dynamic light scattering
EELS	electron energy loss spectroscopy
DSC	differential scanning calorimetry
FTIR	Fourier transform infrared resonance
PID	photoionization detection
XPS	X-ray photoelectron spectroscopy
GFC	Gel-filtration chromatography
LINE-1	long interspersed nuclear elements
cDNA micro-array	differential RNA expression (DE) by cDNA hybridization library
qRT-PCR	quantitative RNA reverse transcription DNA polymerase chain reaction
RNA-seq	RNA sequencing
gRNA-seq	guide RNA sequencing
FPG	formamidopyrimidine glycosylase
SOD	superoxide dismutase
UHCL	unsupervised/unlabeled data hierarchical clustering analysis
GO	gene ontology DE pathway analysis
IPA	ingenuity pathway analysis
IPRL	isolated perfused rat lung
QAR	quantitative autoradiography
DCE-MRI	dynamic contrast-enhanced 1H-nuclear magnetic imaging
FFE	fast-field echo, gradient-recall (GE) echo
SE	spin-echo, fast-spin echo
GKM	generalized kinetic model
PAR	population attributable risk
RR	relative risk
OR	odds ratio
SMR	standardized mortality ratio
HR	hazards ratio
Logit	logistic probability
HA	alternate hypothesis
HO	null hypothesis
ꭓ2	Chi-Square non-parametric
FDR	false discovery rate
REL	recommended exposure limit
PEL	permissible exposure limit
TLV	threshold limit value
AL	action level
TWA	time weighted average
PPE	personal protective equipment
IARC A1	Human carcinogen
A2	Suspected human carcinogen
A3	Confirmed animal carcinogen
A4	Not classifiable as a human carcinogen
A5	Not suspected as a human carcinogen
ILO	International Labor Organization
OSHA-DOL	Occupational Safety and Health Administration
NIOSH-CDC	National Institute of Occupational Safety and Health
ACGIH	American College of Governmental Industrial Hygienists