List of pros and cons for different THz sources.
\r\n\tThe LED can be lingering further into three major categories are (i) Traditional inorganic LEDs, (ii) Organic LEDs (Small Molecule OLED, Polymer LED, Passive Matrix OLED Active Matrix OLED), (iii) High brightness LEDs, (iv) Deep-UV LEDs, (v) Active Matrix Organic Light-Emitting Diodes (AMOLED).
",isbn:"978-1-83968-886-7",printIsbn:"978-1-83968-885-0",pdfIsbn:"978-1-83968-887-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"97e861d1556a639f0e5cc6ee8bdb0a0f",bookSignature:"Prof. Jagannathan Thirumalai",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10559.jpg",keywords:"Aluminum Gallium Arsenide, Gallium Arsenide Phosphide, Indium Phosphide, Thin-Film-Display, Organic Rare-Earth Complexes, Colour Rendering Index, High Brightness Leds, Luminous Control, Air Purification, Skin Therapy, Organic Compounds Form the Electroluminescent Material, Specific Type of Thin-Film-Display",numberOfDownloads:4,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 21st 2020",dateEndSecondStepPublish:"October 19th 2020",dateEndThirdStepPublish:"December 18th 2020",dateEndFourthStepPublish:"March 8th 2021",dateEndFifthStepPublish:"May 7th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"As an expert in the optoelectronics and nanotechnology area, Dr.Thirumalai has been invited to examine several MSc and Ph.D. theses, invited to give a talk in various forums, and to review papers for international and national journals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"99242",title:"Prof.",name:"Jagannathan",middleName:null,surname:"Thirumalai",slug:"jagannathan-thirumalai",fullName:"Jagannathan Thirumalai",profilePictureURL:"https://mts.intechopen.com/storage/users/99242/images/system/99242.png",biography:"Dr. J. Thirumalai received his Ph.D. from Alagappa University, Karaikudi in 2010. \n\nHe was awarded the Post-doctoral Fellowship from Pohang University of Science and Technology (POSTECH), Republic of Korea, in 2013.\nHe worked as an Assistant Professor of Physics, B.S. Abdur Rahman University, Chennai, India (2011 to 2016). \nCurrently, he is working as an Assistant Professor & Head of the Department of Physics, SASTRA Deemed to be University, Kumbakonam (T.N.), India. \n\nHis research interests focus on luminescence, self-assembled nanomaterials, thin-film optoelectronic devices & Supercapacitors. \n\nHe has published more than 60 SCOPUS/ISI indexed papers, 11 book chapters, and he edited 5 books. He is serving as a member in various national and international societies. 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Mahmoud"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. 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by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"51245",title:"THz Measurement Systems",doi:"10.5772/63734",slug:"thz-measurement-systems",body:'\nTerahertz (THz) spectrum refers to the frequency domain ranging approximately from 100 GHz to 10 THz, corresponding to wavelengths from 3 mm to 30 μm (T-waves). The lower limit is the microwave region, where mobile and satellite systems operate, and the upper limit is the far infrared, widely used for optical communications. Figure 1 shows the characteristics of the T-waves in the electromagnetic (EM) spectrum.
\nPosition of the THz waves in the electromagnetic spectrum.
Terahertz frequency region is often defined as the last unexplored area of the EM spectrum, since it represents an area of convergence between electronics and photonics presently lacking a mature technology. Since T-waves are located between microwaves and far-infrared waves, there are two enabling technologies that can be considered for their full exploitation: electronics and photonics. Over the past 20 years, the full access and exploitation of this frequency window have been the objective of intense research efforts both in academia and in industry, in order to close the so-called THz gap. T-waves present many important properties potentially with a shattering impact both in science and in many real-world applications. First, they are not perceptible by the human eye, are not ionizing, and have the ability to cross many non-conducting common materials such as paper, fabrics, wood, plastic, and organic tissues [1, 2]. Then, in terms of energy, they give access to the rotational and vibrational modes of many molecules and macromolecules. These modes can be observed as absorption peaks in the THz spectra, providing the “fingerprint” of unknown compounds via spectroscopic measurements. Finally, the use of THz radiation allows contactless and non-destructive analysis of the materials under investigation, by spatial imaging operations [3–5] with resolution higher than micro- and millimeter waves. THz science can be applied in so many and different areas of interest, from biology to physical, chemical, pharmaceutical, and environmental research, within a broad range of industries including the medical, security, cultural heritage, manufacturing, and aerospace sectors.
\nThe objective of this chapter is to provide readers who are not familiar with the basics of this breakthrough technology, a brief review of the typical architectures of measurement systems and the different sources and detectors that are commonly used, and looking at possible applications. Successively, they will provide details on the parameters that define the performance of a THz system, the measurement methods, and the related errors and uncertainty, focusing at the end on the use of time-domain spectroscopy for the evaluation of different material properties in this specific frequency region.
\nSince its early beginning, one of the main hurdles for the development of THz technology was a lack of solid-state signal sources, rather than detectors. Since T-waves are located between microwaves and far-infrared waves, there are two enabling technologies that can be considered for their emission: electronics and photonics.
\nOne can roughly group THz sources into two major operational families based on the emission mode and the operating frequency: the continuous wave (CW) and the pulsed (or time domain – TD) mode [1, 6].
\nMost of CW systems have been developed from the electronics side, in particular the microwave field. The typical way to generate THz emission, in fact, is by scaling the frequency by using frequency multipliers. These CW systems usually operate in the lower frequency range of the THz band with maximum frequencies around 0.8 THz. Still, there are some systems able to emit at frequencies as high as 5 THz, for example backward-wave oscillators and quantum cascade lasers. Nevertheless, CW systems could be realized from the photonics side too, since down-conversion is possible by mixing two lasers that work at different frequencies [6].
\nCharacteristics of the two major operational families for the THz sources: continuous wave and time domain.
These systems are capable to operate at a single frequency, and their emission is continuous or modulated up to GHz frequencies. Therefore, CW systems are intrinsically narrowband and have often a limited tunability with a high spectral resolution (∼100 MHz) very useful for gas-phase spectroscopy. Moreover, CW systems typically provide output power higher than pulsed sources. They can be passive or active; in the first case, the system detects the radiation emitted by the sample or body, whereas in the second case, the system illuminates the sample and detects the reflected or transmitted radiation. CW systems can be used in telecommunications applications and non-destructive evaluation (NDE) applications.
\nIn pulsed or time-domain (TD) systems, the distinctive element is an optical-to-THz signal conversion technology, based on the generation and detection of an electromagnetic transient having duration of few picoseconds by means of ultrafast pulsed lasers [6]. The short pulse is composed of many frequencies, which can be accessed with a Fourier transform of the pulse. Contrary to what happens in CW systems, pulsed systems can be only active, are broadband in nature, and do not benefit of a continuous emission, so that they are ideal to study ultrafast phenomena and for general purpose spectroscopic applications.
\nIn Figure 2, the major differences between CW and pulsed systems are summarized.
\n\nIn the following, the most commonly used THz sources will be described, with some emphasis on those based on electro-optical conversion, since they form the base for the development of coherent systems operating in the time domain.
\nThe backward-wave oscillator is one of the most important and successful sources based on up-conversion, allowing to extend the frequency of microwave sources to the THz range using harmonic generation. The mechanism of a BWO is very similar to a travelling wave amplifier, with the difference that it is a slow-wave structure, deliberately designed to provide feedback. In particular, a BWO seems a sophisticated high vacuum diode, where the cathode is heated by a low voltage heater and emits electrons accelerated by a high-voltage field travelling toward the anode. The electrons are collimated by a uniform external magnetic field and pass over the slow-wave structure like a comb. This mechanism produces the required quantity for the transfer of the kinetic energy of the electrons to an electromagnetic wave that builds up from noise fluctuations. The most important advantage of BWOs is their tunability; in fact, the tuning rate is ∼10 MHz/V for low frequency devices, rising to ∼100 MHz/V for devices operating at 1 THz or above. In addition, the output power of each BWO varies quite rapidly with frequency and the useful tuning range is approximately ±10% from the centre frequency [7].
\nThe quantum cascade laser belongs to the semiconductor-based THz sources. Over the years, the enormous progress in the field of nanotechnology is making QCL the most employed source in the CW family. In typical semiconductor lasers, light is generated by the recombination of electrons in the conduction band with holes in the valence band, separated by the gap of the active material. In QCLs, the presence of coupled quantum wells (QWs) splits the conduction band by quantum confinement into a number of distinct sub-bands. In fact, the structure of a QLC is composed of semiconducting layered QWs from hundreds to thousands (like InGaAs/AlGaAs) [8]. Applied electric field, lifetimes, and tunneling probabilities of each level are fundamental to obtain population inversion between two sub-bands in a series of identical repeat units. The output radiation frequency is defined by the energy spacing (or QW thickness) of the lasing sub-bands. The active regions are connected with injector/collector structures allowing electrical transport through injection of carriers into the upper laser level and extraction of carriers from the lower laser level [9, 10].
\nStimulated terahertz amplified radiation (STAR) is compact and technologically simple all solid-state emitters based on superconducting devices. Coherent electromagnetic waves are generated at THz frequencies because of the Josephson effect between one or more superconductor–insulator–superconductor (SIS) junctions. Biased by a DC voltage V, a Josephson junction is essentially a two-level system with energy difference of 2 eV. As Cooper pairs are tunneling, EM waves are emitted from the junction. The radiation from a single junction, however, is only about 1 pW, and the frequency is below THz, limited in conventional low Tc superconductors by the small superconducting energy gap. The radiation power can be enhanced by fabricating an artificial array of Josephson junctions. Nevertheless, the crucial aspect relies on the coherent emission of EM waves, which requires synchronized oscillations of individual elements. The synchronization looks easier to achieve for the so-called intrinsic Josephson junctions (IJJs) that are densely packed inside high-quality single crystals of a high transition temperature Tc superconductor, usually Bi2Sr2CaCu2O8+δ (BSCCO). IJJs are formed naturally in BSCCO, where Bi-Sr-O layers between the superconducting CuO2 layers act as a non-conducting barrier of nanoscale thickness [11]. The main feature rendering the STAR emitters so attractive is the nature of the emitted radiation, which is fairly robust, reasonably intense (∼ μW), and characterized by high spectral purity. Furthermore, the line width is so sharp that its observation is limited by the resolution of the conventional spectrometers (0.25 cm−1). Then, the frequency of the THz radiation can be tuned considerably, approximately up to 10–15% of the central frequency, by varying the bias voltage applied to the N IJJ stack, even if its variable range of frequency strongly differs from sample to sample, depending on the preparation conditions [12].
\nA photoconductive antenna is the most commonly used source in THz TDS systems. It consists of a semiconductor substrate where two metallic electrodes are deposited and separated by a gap of few microns. The substrate is typically a direct III-V semiconductor such as GaAs or low-temperature grown GaAs, sometimes doped silicon is adopted. In PCA, photocarriers are produced by the laser pulse and then accelerated by a bias field applied across the gap. In order to generate photo-induced free carriers, it is necessary that the photons of the laser pulse have an energy higher than the bandgap of the semiconductor. If the laser is focused at the gap between the electrodes, the photo-induced carriers are accelerated by the bias field across the gap, which generates a current. The amplitude of the current is a function of time, and thus, the derivative of the current respect time generates the THz pulse, in fact, for the pulsed nature of the laser beam [13, 14]:\n
Sketch of the THz emission mechanism induced in a photoconductive antenna by a fs laser pulse.
Performances of PCAs can be appreciated considering some important and fundamental aspects as: semiconductor bandgap, carrier life and mobility, antenna gap and bias field. Finally, PCAs structures can be resonant or non-resonant. The first ones generate THz radiation around a central frequency, which depends on the gap distance, and the second ones have variable gap distances and provide broader frequency THz emission [13, 15].
\nAnother type of THz source is based on the electro-optical rectification. It is a nonlinear optical effect, and THz waves are generated as a result of a difference-frequency process between the frequency components contained in the femtosecond laser pulse, occurring in materials having a higher order susceptibility that is different from zero.
\nMathematically, the polarization induced by the electric field associated with the optical pulse can be expressed in power series [16]:\n
EO rectification comes from the second term in the previous equation. If the incident light is a plane wave, the polarization induced by the second-order susceptibility can be expressed as:\n
Here, Ω is the difference between two frequency components of the laser pulse, whereas χ(2) is the second-order susceptibility, depending on the material crystalline structure. The radiated electric field due to the EO induced polarization can be expressed as follows [17, 18]:\n
It is worth to emphasize that in EO rectification, no bias is necessary to realize THz generation (Figure 4). For a given material, the radiation efficiency and bandwidth are affected by factors such as thickness, laser pulse duration, absorption and dispersion, crystal orientation, and phase-matching conditions [19].
\nSketch of the electro-optical mechanism.
The term “photomixing” describes the generation of T-waves as a difference frequency in a nonlinear element. In the case of the THz region, it is necessary to use two IR or two visible laser photons, so that the laser frequency difference lies in this frequency range. Basically, a THz photomixer consists of two independently tunable sources (usually, solid-state diode lasers) lighting a photoconductive antenna PCA and yielding the desired difference frequency by heterodyning [20], as shown in Figure 5.
\nOperation of a THz photomixer.
The device serves as a terahertz coherent wave emitter. Changing the temperature or the operation current of the laser diodes, the value of the beat frequency can be straightforwardly regulated.
\nThe band of the photomixer depends on the spectral width of the employed diodes; in particular, in order to enlarge the band or to move the band of the photomixer into higher frequencies, diodes with different central frequencies are necessary [21].
\nTable 1 summarizes the major strengths and weaknesses for the operation of the sources reported above in the THz region.
\nSource | \nAdvantages | \nDrawbacks | \n
---|---|---|
BW oscillator | \n
| \n
| \n
QC laser | \n
| \n
| \n
STAR emitter | \n
| \n
| \n
Photoconductive antenna | \n
| \n
| \n
Electro-optical conversion | \n
| \n
| \n
Photomixing | \n
| \n
| \n
List of pros and cons for different THz sources.
In general, detectors are transducers converting an input signal into some convenient form that can be observed, recorded, and analyzed. In case of THz technology, the signal is an electromagnetic wave whose amplitude and phase both hold important information. According to the nature of EM wave, detectors can be grouped into two classes: incoherent or direct detectors that detect the amplitude only and coherent detectors that detect both amplitude and phase.
\nThe performance of a THz detector depends on a number of parameters, some of them correlated among them. The most important are as follows: bandwidth (the spectral range over which the detector responds), responsivity (input–output gain of the detector system), noise characteristics (characterized by the noise equivalent power, NEP, that is the signal power required to yield a signal-to-noise ratio of unity at the output of the detector in a 1 Hz bandwidth), dynamic range, response time, and sensitive area.
\nThe photon energy of a THz wave is relatively weak and comparable to the phonon energy of a crystal; for this reason, it could be detected as thermal energy. In principle, the response of such thermoelectric detectors is very slow—in the order of milliseconds—because of the thermoelectric reaction of the crystal, but it is usually ultra-broadband.
\nThe most commonly used thermoelectric detectors are as follows: Golay cell, pyroelectric, and bolometer.
\nThe Golay cell is a gas cell detector in which an IR-absorptive gas is encapsulated, and its thermal expansion produced by THz waves is optically detected. The pyroelectric detector is a photovoltaic detector made with a dielectric material, exhibiting temperature-sensitive surface polarization and high sensitivity to THz imaging. Typical value of NEP is 100 pW/√Hz. Finally, the bolometer is a temperature-sensitive semiconductor, such as Si or Ge, and detects the THz radiation as the change in resistivity by the heating due to absorption of THz radiation. The bolometer could operate at cryogenic temperatures, ultimately minimizing the background noise level. The typical value of NEP is
The mechanism of a modern version of this detector, with its main features, is shown in Figure 6.
\nThe main component is a sealed cell where a gas having a low thermal conductivity, usually xenon, is inserted. One side of the cell ends with a window that allows the transmission of the THz radiation in the adopted frequency interval. The other side instead is closed with a flexible mirror. The cell is completed with a thin absorbing metallic film whose impedance nearly matches that exhibited by free space. The metallic film absorbs the THz radiation thus heating the surrounding gas and causing a slight movement of the mirror. This displacement is subsequently converted into an electrical signal. In particular, a lens system is exploited to concentrate the light emitted by the source through a grid towards the mirror; then, the mirror reflects the light back to the detector through the grid. If a displacement of the mirror occurs, the reflected image of the grid is distorted, and thus, the amount of light reaching the light detector changes. For THz frequencies, the most useful window materials are high-density polyethylene (HDPE), high-resistivity Si, crystalline quartz, and diamond [22].
\nStructure of a Golay cell.
A pyroelectric detector (or pyrometer) is an ac thermal sensor characterized by a frequency response spanning a large frequency range, which includes the THz region. It is based on the pyroelectric effect exhibited by a thin, permanently poled, ferroelectric crystal (i.e., LiTaO3), in which the instantaneous polarization is dependent on the rate of change of the crystal temperature. Pyrometers are commercially available either as single devices or as arrays for the entire IR and THz spectral regions. Besides being very sensitive, they have many advantages, including being relatively cheap and rugged, with room temperature operation. Their most useful property is that, with appropriate design of an associated amplifier, they can exhibit response times ranging from milliseconds to less than a nanoseconds [23]. In Figure 7, the scheme of a pyroelectric detector and its equivalent circuital model is presented.
\nPyroelectric detector and equivalent representation of electric circuit diagram.
Semiconducting bolometers are among the most important of THz detectors. A design for a bolometer is shown in Figure 8. It consists of a small chip of doped semiconductor, Si or Ge. Typical doping concentrations are 1016 cm−3 for Ge and 1018 cm−3 for Si. The detector element is suspended in vacuum by two thin lead wires between the electrical contacts, which provide the electrical connections as well as the thermal link to the heat sink. Two aspects determine the optimum level of doping: (i) the temperature coefficient of the resistance should be large and (ii) the bolometer should have a resistance that allows for an efficient coupling to a low noise amplifier [24].
\nSimple design of a semiconducting bolometer.
A specific bolometer is the superconducting hot-spot air bolometer (SHAB), where the detector element consists of a microscopic narrow Nb or NbN strip creating a free-standing bridge structure on top of a substrate. When a voltage bias is applied to the structure, this produces the formation of a hot spot in the middle of the strip where the incoming radiation energy is dissipated, thereby switching from the superconducting to the normal state. The overall effect is a modulation of the suspended strip resistance, with a consequent modulation of the current flowing because of the voltage bias. From the recorded current, one can extract a measure of the THz radiation [25].
\nA photoconductive antenna could be considered also to detect THz waves. The structure is very similar to the structure of PCA for emission. In this case, PCA as detector measures the photocurrent, which is collected by the electrons generated by the probe beam across the antenna gap and biased by the THz electric field. When no electric field is present, the photocarriers produced by the laser pulse move randomly and no current is observed. On the contrary, when the THz wave irradiates the gap, it generates an electric field separating electrons from holes, and therefore, a current, proportional to the amplitude of the electric field, is observed. It is important to emphasize that PCA for detection and PCA for emission are differently designed. The narrower is the gap, the lower is the electric field required for obtaining an appreciable current; therefore, PCA for detection exhibits narrower gaps (∼10 µm) when compared with typical gaps of PCA for emission (> 50 µm) [14, 26, 27]. Factors affecting the performance of a PCA are similar to those for the emitter: semiconductor bandgap, carrier lifetime and mobility, and antenna gap [14].
\nElectro-optical (EO) sampling is based on the Pockels effect, in which the application of an electric field, on a material, induces or modifies the birefringence properties of it. In other words, the Pockels effect is a change in the refractive index or birefringence that depends linearly on the electric field. It is important to say that the Pockels effect can be observed only in crystals characterized by no inversion symmetry, like those belonging to the zinc-blende group such as the ZnTe [19, 27].
\nUsing this detection method, the THz electric field is sensed by measuring the change of the birefringence properties of the crystal, caused by the field itself. These changes can be measured by analyzing the polarization properties of an optical probe beam going through the crystal. The most common setup to measure the THz waveform with EO sampling is a balanced measurement approach, as shown in Figure 9.
\nSketch of the electro-optical sampling.
The operating principle is as follows. An optical probe beam characterized by linear polarization is first passed through a polarizer and then propagates within the EO crystal. A quarter-wave plate (QWP) is positioned just after the EO crystal in order to modify the probe beam ellipticity; moreover, a suitable Wollaston prism is exploited to split the elliptical polarization into its two perpendicular components. A proper photodiode assembly mounted in differential configuration detects the diverse polarization intensity. In the absence of THz radiation impinging on the EO crystal, the probe beam ellipticity can be regulated in such a way that both polarization intensities are equal; as a consequence, the net current flowing from the differential photodiodes is equal to zero. On the contrary, when the THz wave is present, the birefringence of the EO crystal is modified by the electric field, thus changing accordingly the ellipticity of the probe beam. As a result, the balance between the two polarizations is broken and the photodiodes assembly can generated a net current whose intensity is proportional to the amplitude of the electric field associated with the impinging THz wave.
\nTable 2 summarizes the major strengths and weaknesses for the operation of the detectors reported above in the THz region.
\nDetector | \nAdvantages | \nDrawbacks | \n
---|---|---|
Golay cell | \n
| \n
| \n
Pyrometer | \n
| \n
| \n
Bolometer | \n
| \n
| \n
Photoconductive antenna | \n
| \n
| \n
Electro-optical crystal | \n
| \n
| \n
List of pros and cons for different THz detectors.
As already described in Section 2, the operation of THz systems can be schematically divided into continuous wave and pulsed mode.
\nFrequency domain (THz-CW) systems work at a fixed frequency, which depends on the type of THz emitter. As an example, in Figure 10, the case of a QCL source coupled to a thermal detector (a pyrometer or a bolometer) is shown. Between THz emitter and detector, there is an ellipsoidal mirror that allows to collimate the laser beam. In this configuration scheme, the reference signal produced by the source and the output signal recorded by the detector is sent to a lock-in amplifier in order to provide a coherent detection [28].
\nSketch of a THz-CW system. A QCL is used as THz source and a bolometer or a pyrometer as THz detector.
In the following, the attention will be focused primarily on the characteristics and performance of THz measurement system working in the time domain (THz-TD). A typical architecture is shown in Figure 11.
\nTypical scheme of a THz-TD system in transmission mode.
A beam splitter is adopted to divide the ultrafast optical pulse generated by an ultrafast laser into two beams, referred to as probe and pump, respectively. T-ray pulsed radiation is stimulated at the emitter by the optical pump beam via either charge transport or optical rectification effect, according to the specific type of exploited emitter. A suitable set of lenses and a pair of parabolic mirrors is adopted in this configuration to collimate and focus the diverging T-ray beam on the sample of interest. A similar combination of lenses and mirrors is then needed to recollimate and focus onto the receiver the T-ray beam passed through the sample. At receiver side, the originally split probe beam acts as an optical gate for the T-ray receiver; the optical gate signal is characterized by a shorter time duration compared with that of the arriving T-ray pulse. It is worth noting that a proper synchronization between the gating and T-ray pulse is mandatory to assure T-ray coherent detection at a time instance. By carefully controlling the optical delay line by means of the proper micromotion of a mechanical stage, it is possible to achieve a complete temporal scan of the T-ray signal [29].
\nReflection measurements can be also used for practical applications, when bulky samples are considered, that are impossible to measure in a transmission mode (see Figure 12) [30].
\nTypical scheme of a THz-TD system in reflection mode.
In this paragraph, the various characteristics and limitations associated with a THz system, in particular when defining its performance, are discussed. The parameters commonly used are the dynamic range (DR) and the signal-to-noise ratio (SNR), which mainly affect the accuracy in THz measurements. They should always be evaluated during the measurements, to avoid false interpretation of the results; therefore, some recommendations for the best practice are presented [31].
\nThe dynamic range (DR) is defined as the ratio between the highest and lowest measurable signal and therefore describes the maximum signal change that can be quantified. As a matter of practice, it is calculated as the ratio between the maximum signal amplitude and the root-mean-square of noise floor. The signal-to-noise ratio (SNR) is defined as the ratio between the mean peak amplitude and the standard deviation (SD) of the signal amplitude. It indicates the minimum detectable signal change and is a complementary system parameter with respect to the dynamic range. Therefore, the DR determines the measurement bandwidth, whereas the SNR reflects the amplitude resolution or sensitivity.
\nDR and SNR may be evaluated either with respect to the time-domain waveform or to the spectrum obtained through Fourier transform. Two specific aspects are involved when these parameters are considered. First, data are acquired as time-domain waveforms, whereas measured optical parameters are derived from the Fourier transform (FT) spectra. In particular, the DR and SNR of time-domain signals may result different from that of the corresponding spectra, and there is not a straightforward analytical relationship between the parameters estimated according to the two methods. Moreover, both DR and SNR of spectral data are strongly frequency dependent and typically decrease steeply with frequency.
\nThe recommended procedure for estimating the DR and SNR of THz through time-domain data is based on the following steps:\n
Acquiring the time-domain waveform and measuring the maximum peak value.
Acquiring the noise signal in the absence of THz, for example, before the arrival of the main pulse.
The mean signal in the absence of THz should be constant (zero for electro-optic detection, nonzero for a photoconductive antenna). However, its standard deviation (SD) has to be measured.
DR is given by the ratio between the mean value of the measured peaks and the SD of the noisy signal.
SNR is given by the ratio between the mean value of the measured peaks and their SD.
The recommended procedure for estimating the DR and SNR of THz through the amplitude spectrum is based on the following steps:\n
Calculating the FTs of a defined number of time-domain records, their mean and SD, and estimating the noise floor of the mean FT. If only the DR is required, one FT spectrum is sufficient.
DR is given by the ratio between the mean FT amplitude and the noise floor.
SNR is given by the ratio between the mean FT amplitude and the SD.
The sampling frequency and observation interval of the time-domain acquisition have to be constant throughout the procedure, because both affect the SNR and the DR.
It is desirable to test the performance of the system by varying the scan parameters in order to identify the ranges characterized by the best SNR and DR values.
If DR and SNR are evaluated through the FT, the signal averaging is recommended, in order to reduce the noise effects. It is worth to notice that if a jitter affects the peak position, which often is due to errors in the initial position of the delay stage, the time-domain average will be distorted; the FT amplitude spectra and their average value, on the contrary, will remain correct. As expected, the approach for the evaluation of DR and SNR of a THz TDS system strictly depends on the specific domain adopted for the measurements. In other words, if the measurements are carried out in time domain, then the DR and SNR must be directly calculated from the available time-domain data. On the contrary, DR and SNR must be evaluated from FT amplitude spectra when measurement-based spectroscopic data are taken into account.
\nAnother fundamental parameter to describe characteristics of a THz system is the spectral resolution. It is determined from the span of the time delay sweep, and it is given by the ratio between the light velocity c and the effective delay line length (multiplied by 2).
\nIn principle, the pump laser pulse repetition rate is the only limitation of resolution if the ideal conditions of noise-free system and unlimited delay line are met. On the contrary, the actual frequency resolution is hardly reduced in presence of noise and mainly influenced by the time-domain SNR of the system. For increasing length of the delay line, the signal amplitude is reduced because of the increasing delay from the main pulse, and the SNR approaches unity.
\nAs expected, THz pulses in the train must be identical with one another to make optical sampling works successfully. If this condition is not satisfied (i.e., the evolution of THz pulse shape occurs on time scales comparable to (or shorter than) the measurement time), no reliable samples of the waveform can be acquired. Besides this main drawback, another minor disadvantage affects the performance of optical sampling. As for the other sampling techniques, optical sampling also takes long time to acquire the desired data. The lower bound for the acquisition time is given by N * ∂t, (N and ∂t being, respectively, the number of measured electric fields and the train pulse-to-pulse distance). Since it is possible to take advantage of signal averaging, the acquisition time is usually much longer than this minimum value. Another problem inherent to sampling measurements is that they require a method for varying the delay of the sampling gate relative to the THz pulse. This requirement is often accomplished by means of a mechanical delay line consisting of a mirror that is moved to vary the optical path length [1].
\nA typical device used for calibrating the linearity of amplitude/power measurements of THz system has to exhibit constant loss within the THz bandwidth. The most convenient and preferred solution is the employment of optically flat silicon plates as loss elements. Fresnel reflections are solely responsible for transmission losses in such a plate. Using a stack of plates parallel to each other, orthogonal to the incident THz beam and separated by air gaps, one can manage the loss level since it is dependent on the number of plates in the stack.
\nWhen the device is placed in the beam path, particular attention has to be paid to avoid distorting the THz beam or altering its focusing; in fact, it is desirable to position the device in a collimated beam. The device can be used in both THz CW and THz TDS systems, where the plates must be angled to the incident beam so as to destroy the etalon interference. There are two methods to verify the linearity and either time domain or frequency domain data can be processed:\n
In the time-domain method, the signal is processed in order to identify the peak value of the amplitude signal. Then, the obtained peaks are plotted in a semilog graph versus the number of Si plates in the beam path. The system is supposed to be linear if the plotted curve exhibits a linear behavior characterized by a slope equal to 0.7.
The second method of testing a TDS involves the calculation of THz spectra. The amplitude at chosen frequencies is plotted against the number of Si plates in the beam path. As in the time-domain method, the semilog plots are expected to be linear with a slope of 0.7.
It is worth noticing that the linearity of a TDS should not be assumed, but it should be experimentally verified.
\nMany sources of error can affect a THz-TDS measurement and data extraction. As for example, laser intensity fluctuations, optical and electronic noise, delay line stage jitter, registration noise are common error sources. Moreover, contributions to the error in the estimated optical constants are not only from the randomness in the signal, but also from imperfections in the physical setup and parameter extraction process. Examples are the sample thickness measurement, the sample alignment, and so on. Significant sources of error are shown in the scheme of Figure 13. The uncertainty sources (green lines) can influence both the THz-TDS measurements and the parameter extraction process. Each of them determines a variance that can be propagate along the process and determine a variance on optical constants [32, 33].
\nScheme showing the propagation of uncertainties in THz-TDS measurements (taken from Ref. [33]).
Two major sources of thermal noise can be singled out in a THz-TD system:\n
Johnson-Nyquist noise is generated when charge carriers fluctuate in a substrate. It results in an artifact voltage measured with no T-ray incident electrical field, whatever is the presence of optical gating pulses.
Background noise gives rise to a random voltage across the receiving antenna.
Other relevant noise sources are quantum fluctuations and laser and shot noise. One of the most efficient methods to remove noise in a T-ray signal is by means of digital signal processing technique such as wavelet de-noising that can actually improve the signal SNR, particularly when intensities are strongly reduced in biological samples. Ultimately, the noise in a THz-TDS system limits the spectral resolution; the best achievable resolution in a defined frequency interval depends, in fact, on the maximum time duration, which is, in turn, directly related to the actual SNR within that frequency range. In this way, improvement of the system dynamic range (obtained by either increasing the power of the transmitted THz waveform or decreasing the system noise floor) turns out to be mandatory to assure suitably high-frequency resolution.
\nAnother uncertainty source is the positioning of the stage for optical delay line (ODL); thanks to the exploitation of a couple of moving mirrors, ODL mechanically induces a delay either on the probe or, equivalently, pumping pulse. As a consequence, the sampling time of the optically-gated detector is characterized by uncertainty; due to its combination with the other sources (electronic and optical noise), a final uncertainty on the amplitude measurements of the sampled T-ray pulse arises. The uncertainty associated with the amplitude of the acquired T-ray pulse affects also the spectral components obtained through the Fourier transform and the deconvolution process. Another uncertainty source that cannot be neglected involves the procedure for unwrapping the phase. Moreover, the thickness and the alignment of the samples have to be known in order to extract the model parameters; as a consequence, the uncertainty associated with these inputs affects the whole parameter detection process. Finally, the overall uncertainty is affected by the uncertainty associated with the estimation of the air refractive index. Each uncertainty source, however, can be taken into account by means of a proper model describing the uncertainty propagation in the measurement process.
\nThe term spectroscopy refers to a series of experiments aiming to investigate the excited states of a specimen, exploiting the interaction of a proper electromagnetic perturbation with a sample. Reflected and/or transmitted waves release specific information on the electromagnetic properties of the sample as function of the frequency. Therefore, according to the spectral content of the electromagnetic signal-probe, different excitations can be investigated ranging from the quantum properties (energy levels of atomic bonds, roto-vibrational states, etc.) of molecules to the impedance of a macroscopic samples or transmission lines. The THz band is ideal to study electrodynamic properties of materials from metals to insulators, since the frequency is lower than the typical plasma frequency of metals (about 1015 Hz) that defines the frequency above which the metal becomes transparent to radiation. Coherent THz radiation can provide valuable information on the complete set of the complex electrodynamic parameters [34] (refractive index (ñ) permittivity (
The peculiar characteristic of using a TDS consists into directly manipulating the time-dependent electric field E(x, t) transmitted through the sample. The ratio between the Fourier transforms of the transmitted signal and the reference signal is directly function of the refractive index. The sketch of the measurement on a generic sample L thick is reported in Figure 14. E(x,t) propagating from the transmitter (Tx) to the receiver (Rx) is linearly polarized along y. Since the signal is generated and detected in air, the proposed scheme allows to generalize the measurements in multilayer samples.
\nScheme of the measurements through the THz-TDs system. Tx and Rx stand for transmitter and receiver, respectively. L represents the mean size of the sample, while ni with i = 1, 2, 3 are the refractive index of different through THz pulse.
The transmitted signal through the sample S(ω), can be expressed as function of the Fresnel coefficients Ta,b (ω) = 2ña/(ña + ñb) and Ra,b (ω) = (ña – ñb)/ (ña + ñb) and the propagation factor Pa(ω, d) = exp{−i ña ω d}, where the labels refer to the material [35]. The complete signal can be expressed as:\n
Time-dependent signal measured through THz-TDS system. The black curve is the reference signal acquired in free space, whereas the red curve is the signal through a Si sample of 500 μm thick. The arrows indicate the primary signal copies generated by the Fabry–Perot effect.
Eq. (1) also defines the transmittance\n
The transfer function H(w) in Eq. (7) is used as theoretical reference for T(ω) to calculate optical parameters of samples. Eqs. (6) and (7) describe the transmission through a homogeneous slab with refractive index ñ2 when the equivalence ñ1 = ñ3 ñair is verified. Instead by putting ñ1 = ñair, Eqs. (6) and (7) are able to describe a system composed by two layers as a thin metallic film on a dielectric substrate [36, 37].
\nSeveral techniques [36–40] have been developed in order to extract ñ by computing the minimum difference between the moduli and the phases of H(ω) and T(ω):\n
Eq. (9) allows to define an error function, the total variation (TV) [38], defined by the sum of differences δ1 and δψ for each frequencial point\n
This is a tridimensional paraboloid as function of the frequency and the sample thickness. The computational search of the minimum of ER(L, ω) implies the contemporary knowledge of the main quantities describing the system: the sample thickness L the refractive index n, and the extinction coefficent k [39]. A fast resolution of the TV approach is affected by the noise in the measured spectrum of T(ω). The most relevant noise source in thin samples is the Fabry–Perot oscillations which show a frequency inversely proportional to L. This problem can be overcome imposing the quasi-space (QS) optimization [39], where the periodicity of the FP effect is employed to achieve the effective optical thickness of the sample. The quasi-space is defined by the Fourier transform of an electro dynamical parameter y(ωn) which could be the refractive index or the extinction coefficient. Therefore, a new set of variables can be defined as follows\n
This function can be displayed in terms of the variable
Without claiming to be exhaustive, we presented a short overview of THz measurement systems presently under development for scientific and industrial applications. We first described the most common sources and detectors that are routinely in use for the manipulation of T-waves. We then focused on the typical architectures that are presently employed in time-domain spectroscopy and imaging. The importance of metrological aspects in THz systems performance and measurements and most of their related issues and solutions were discussed. Since each material has its own “identity card” in this band of the spectrum, a THz-TDS waveform transmitted through a sample is typically rich in information. We therefore presented what are the main parameters that can be measured from the material frequency response, namely optical or electrical complex quantities like the refractive index, the conductivity and the dielectric constant, and what are the data extraction methods and the related errors and uncertainty.
\nChina has been suffering severe air pollution in recent years, characterized as high levels of fine particles (PM2.5) and ozone [1, 2, 3, 4]. As part of atmospheric composition, air pollutants play important role in climate change. For example, ozone is one of major greenhouse gases, which causes atmospheric warming [5]. Atmospheric aerosol is one of the most important and uncertain factors in both climate change and weather activities. It influences climate by its direct radiative forcing and induced cloud adjustments and weather by the interactions of aerosol-radiation, aerosol-cloud, etc. [5]. Air pollution also leads to adverse effects on health [6, 7], including increasing of respiratory and cardiovascular diseases, excess mortality, and decreasing of life expectancy [8, 9, 10, 11]. High particulate matter (PM) concentration under relatively high relative humidity (RH) conditions often induces haze events and causes high risk on public activities such as surface transportation, aircraft take-off and landing. Therefore, the characteristics, formation mechanisms, and influence factors of air pollution and related issues were seriously focused in recent years (e.g. in [4, 12, 13, 14, 15]).
\nIn policy decision aspect, the Chinese government therefore has issued series of actions to reduce air pollution in the last few years. The new Chinese national ambient air quality standards (CNAAQS2012) [16] was jointly released by Ministry of Environmental Protection (MEP) of the People’s Republic of China and General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China in 2012. At the first time, standards for PM2.5 and daily maximum 8-hour averaged (DM8H) ozone (O3-8h) were established in China. The State Council then issued a stringent action plan to combat air pollution on September, 2013 [17]. China sponsored tens of projects and funded several billions since 2016 in a special fund named Study on Formation Mechanism of Atmospheric pollution and Control Technology. In the support of the Premier Fund, “2 + 26” cities were chosen and one scientific team was organized for each city in 2017 to deal with the air pollution in Beijing-Tianjin-Hebei and its surrounding region. Accordingly, China Meteorological Administration (CMA) established operational centers in three populated regions (Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta) to provide air quality forecasting and warning. Provincial governments took many kinds of actions to try to improve ambient air quality.
\nEastern China, which covers the Yangtze River Delta, is one of the most polluted regions [1, 3]. The air quality in this region is also influenced by Beijing-Tianjin-Hebei region by the northwesterly. Study on air pollution as well as its secondarily produced haze in this region was thus widely carried out and numerical modeling played an important role. For example, Tie et al. studied ozone [18] and Zhou et al. studied particulate matter and haze [19] over Shanghai by using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) [20]; the severe PM pollution and haze episodes over eastern China in January 2013 were modeled by using the nested air quality prediction model system (NAQPMS) [21] and revised Community Multi-scale Air Quality (CMAQ) model [22, 23], etc. In the previous studies, increase of secondary aerosols was certified to take important role in heavy PM pollution events (e.g. in [19, 23, 24]) and some new sources through heterogeneous processes were found to promote rapid increase of PM in extreme pollution episodes [14, 25]. These works proved that the usage of air quality models is one valid solution to air pollution studies.
\nIn this chapter, the numerical forecast of air quality over eastern China is presented. This work is one of the important applications of numerical meteorological prediction and supports air quality and relevant service including temporary emission control and study of air pollution on health, etc. In the next sections, the brief history of development of numerical modeling for air pollution will be reviewed. Then the operational forecast will be emphasized, including the construction of modeling system and forecast performance. Analysis and discussion on the uncertainty and shortage in current work will be presented to help improving the forecast in the future. Brief conclusion will be given in the end.
\nAir quality models are tools that describe the physical and chemical processes which influence air pollutants, including chemical reactions, transport, diffusion, scavenging, etc. in the atmosphere. They are built based on the understanding of atmospheric physics and chemistry and computation technology. The models are used in many air quality and related issues, such as analyzing the characteristics of tempo-spatial patterns and changes of air pollutants, discovering the mechanisms of formation of air pollution, and estimating the influence of the change of factors (e.g. anthropogenic emission, volcanic explosion) on air quality, etc. Usually, air quality models are more or less driven by meteorological variables and therefore are connected with meteorological models or model outputs.
\nSince the 1970s, three generations of air quality models have been developed sponsored by United States Environmental Protection Agency (US EPA) and other organizations. In the first-generation models, atmospheric physical processes are highly parameterized and chemical processes are ignored or just simply treated. These models introduce the dispersion profiles in different levels of discretized stability and are specialized in calculating the long-term average concentration of inert air pollutant. The second-generation models include more complicated meteorological models and nonlinear chemical reactions and the simulation domain is three-dimensionally (3-D) gridded. The chemical and physical processes are individually calculated in each grid and influence between neighbor grids is considered. This generation is used generally to treat one type of air pollution, such as photochemical smog and acid rain. In the end of the 1990s, US EPA presented the concept of “one atmosphere” and developed the third-generation air quality modeling system—Medels-3/CMAQ [26]. It is an integrated system and consists of serial modules to process emissions, meteorology inputs, chemical reaction and transport, production making, etc. The third-generation models involve relatively detailed atmospheric chemistry and physics as well as the influence and inter-conversion among air pollutants of different types or phases. In fact, the divide of different generations is not distinct and some models are still in continuous development. For example, the CALPIFF (one Lagrangian model of the first-generation) introduced much research results in the 1990s and was often implemented in the 2000s. The second version regional acid model (RADM2) increased chemical species and reactions [27] and was introduced in the very newly developed third-generation model of WRF-Chem [20].
\nIn recent years, 3-D chemical transport models (CTMs) has been widely used in studying and forecasting air quality combined with numerical meteorological models benefited from the rapid development of models and computing technology. For example, global ozone was simulated by using the model for ozone and related chemical tracers (MOZART) and the model performance was evaluated [28, 29]. Gu et al. studied summertime ozone and nitrate aerosol in upper troposphere and lower stratosphere (UTLS) over the Tibetan Plateau and the south Asian monsoon region using the Goddard Earth Observing System chemical transport model (GEOS-Chem) [30, 31]. The CMAQ model had a great number of applications around the world, e.g. in [32, 33]. Tie et al. studied the characterizations of chemical oxidants in Mexico City using WRF-Chem [34]. Zhou et al. developed an operational mesoscale sand and dust storm forecasting system for East Asia by coupling a dust model within the CMA unified atmospheric chemistry environment (CUACE) [35]. Zhou et al. developed the CUACE for aerosols (CUACE/Aero) to study chemical and optical properties of aerosol in China [36]. Over eastern China, there were also numerous applications of CTMs. Gao et al. studied regional haze events in the North China Plain (NCP) using WRF-Chem [37]. Zhou et al. built an operational system to forecast air quality over eastern China region and resulted good performance in forecasting the major air pollutants of PM2.5 and ozone over this region [38]. Wu et al. analyzed the source contribution of primary and secondary sulfate, nitrate, and ammonium (S-N-A) during a representative winter period in Shanghai using online source-tagged NAQPMS [39]. Li et al. investigated ozone source by using the ozone source apportionment technology (OSAT) with tagged tracers coupled within Comprehensive Air Quality Model with Extensions (CAMx) [40].
\nAir quality modeling in current generation can be switched “offline” or “online” depending on the treatment of meteorology and chemistry. The offline chemical processes are treated independently from the meteorological modeling, while those in online approach are dependent. The modeling systems implemented in recent years are mostly offline, such as AIRPACT [32]. The chemical transport in this approach is driven by outputs from a separate meteorological model, typically available once per hour. This approach is computationally attractive since only one meteorological dataset can be used to produce many chemical simulations for different scientific questions. On the other hand, the “online” treatment (e.g. WRF-Chem) was newly developed to solve the loss of information in offline approach about atmospheric processes that have a time scale of less than the output time interval of meteorological models, including wind speed, wind direction, rainfall, etc. The lost information may be very important in high resolution air quality modeling. The online approach also benefits to investigate the interactions between meteorology and chemistry [21], which are out of the purpose of offline treatments. Previous studies (e.g. in [19, 21, 37, 38, 41]) on air pollution and related issues over eastern China region had proved the applicability and advantage of the online model of WRF-Chem.
\nShanghai Meteorological Service (SMS), as well as the East China Meteorological Center of CMA, shares the responsibility to provide air quality forecast and air pollution warning for Shanghai and guidance for East China region. Therefore, SMS initialized numerical modeling of air quality in 2006. This work got scientific and technological supports from the World Meteorological Organization (WMO) through Shanghai WMO global atmosphere watch (GAW) urban research and meteorological environment (GURME) Pilot Project. Based on the thinking of the applicability and advantage of WRF-Chem and the extendibility on calculation of the inter-feedback between meteorological variables and air pollutants, WRF-Chem was chosen as the core model in developing our numerical air quality forecast system. An experimental forecasting system was established in 2008, in which nested domains of 16 × 16-km and 4 × 4-km was implemented. The outer domain covered eastern China region and the inner one covered the main YRD region. The evaluation showed that the results from two domains had comparable performance and further study in [34] showed that the 6 × 6-km resolution performed best under the conditions of the model and emission data at that time. Therefore, a real time forecast system covering the YRD region with a horizontal resolution of 6-km was built in 2009 to support the air pollution (including three variables of PM10, SO2 and NO2) forecast for Shanghai. This application showed that the forecasts from this version had acceptable performance under relatively stable conditions but poorer performance for transport cases, because there are much more air pollutants transported from areas outside the model region such as the NCP. With updates in high performance computational resource, one forecast system covering eastern China region was established in 2012, which was named as Regional Atmospheric Environmental Modeling System for eastern China (RAEMS). This system was certificated as an official operational forecast system by CMA in March, 2013. More details about the operational system will be introduced in the next section and the brief history of its development was shown in Figure 1.
\nThe brief history of development of numerical air quality forecast in SMS.
The core model in RAEMS is WRF-Chem, which was developed through the collaboration of several institutes (e.g. NOAA, NCAR, etc.). Chemistry and meteorology is fully coupled in this model, in other words, the same advection, convection, and diffusion scheme, model grids, physical schemes, and time step is used and there is no interpolation in time for meteorological fields. The modeling performance of WRF-Chem has been extensively validated [20, 42]. Several real-time prediction systems were built based on the WRF-Chem model to provide air quality forecasts around the world (e.g. China, the United States, and Brazil), as listed in [43]. In RAEMS, several improvements were made based on WRF-Chem version 3.2 by Tie et al. [44], including the introduction of aerosol effects on photolysis, adjustments of nocturnal ozone losing, and introduction of ISORROPIA II secondary inorganic aerosol scheme [45]. This modified version has been validated, showing good performance in ozone and PM2.5 prediction for Shanghai [18, 19].
\nAs shown in Figure 2, the domain encompasses the eastern China Region. Centered at (32.5°N, 118°E), it consists of 360 un-staggered grids in west-east and 400 in south-north with a 6-km grid resolution. There are 28 layers vertically, with the top pressure of 50 hPa. The time step for integration is 30-s for meteorology and 60-s for chemistry, and these for radiation, biogenic emission, and photolysis are 10, 30, and 15 min, respectively. Physical options are listed in Table 1. Specially, the Noah-modified 20-category IGBP-MODIS instead of 24-category USGS land-use was used. The RADM2 [27] was used for gas-phase chemistry. ISORROPIA II secondary inorganic [45] and the Secondary ORGanic Aerosol Model (SORGAM) [46] schemes were used to treat aerosol chemistry. Madronich scheme [47, 48] was applied for photolysis.
\nComponents of RAEMS. Domain coverage was shown in the central component.
Parameterization scheme | \nOption | \n
---|---|
Micro-physics (mp_physics) | \nWSM 6-class | \n
Cumulus parameterization (cu_phy) | \nNot used | \n
Long-wave radiation (ra_lw) | \nRRTM | \n
Short-wave radiation (ra_sw) | \nDudhia | \n
Surface layer (sf_sfclay) | \nMonin_Obukhov | \n
Land surface (sf_surface) | \nUnified Noah | \n
Boundary layer (bl_pbl) | \nYSU | \n
Gas-phase chemistry | \nRADM2 | \n
Inorganic aerosol chemistry | \nISORROPIA II | \n
Organic aerosol chemistry | \nSORGAM | \n
Physical and chemical configuration in RAEMS.
The global forecast from the National Centers for Environmental Prediction Global Forecast System (NCEP GFS) was used for meteorological initial and boundary conditions. NCEP GFS data was used widely for weather forecast, analysis, and as the initial and lateral boundary conditions of regional modeling. 0.5-degree GFS forecast was used, and 1-degree data was also applied if higher resolution forecasts were not available. Previous forecast was used for chemical initial conditions. The gaseous chemical lateral boundary conditions were based on estimations from a global chemical transport model (MOZART-4) [28, 29]. Boundary conditions were extracted from the MOZART-4 by matching the RAEMS boundary with the MOZART cells. While maintaining diurnal variations in species concentrations, monthly averaged MOZART-4 values of the year 2009 were applied.
\nBiogenic emissions were calculated online using model of emissions of gases and aerosols from nature (MEGAN2, in [49, 50]). Global land cover maps including isoprene emission factor, plant functional type, and leaf area index were applied.
\nThe multi-resolution emission inventory for China (MEIC [51, 52]) for the year 2010 was applied as the anthropogenic inventory. MEIC inventory was developed by Tsinghua University, including emissions of 10 major atmospheric pollutants and greenhouse gases (SO2, NOX, CO, NMVOC, NH3, CO2, PM2.5, PM10, BC, and OC) over mainland China. MEIC supplied gridded monthly emissions from five sectors (industry, power, residential, transport, and agriculture) with a 0.25-degree resolution. Asian emission inventory for the NASA INTEX-B Mission [53] was applied for regions outside mainland China and before August, 2014. It has a resolution of 0.5-degree for the year 2008.
\nWhile being used in RAEMS system, the emissions were spatially regridded to the model grids. Emissions were also hourly allocated with the diurnal profile (in [38]) provided by Shanghai Academy of Environmental Science. NO emission took a proportion of 90% of the amount of NOx in mole number and NO2 took the rest 10% (as in [41]). Information of spatial distribution and total amount can be found in [38].
\nThe RAEMS was authorized as an official operational forecasting system by CMA on Mar. 23, 2013 and has been producing forecast since then. The operational system runs once per day, initialized at 12Z UTC (20Z LST). It is started at about 2 am at local time every day and completes entire simulation and post-processing within 5 h. The predictable time length is more than 78 h and the forecast system provides forecast products for 3 local days.
\nOperational products are displayed on a website [54]. The link to this site is also accessible from the official NOAA WRF-Chem website [43]. The products include hourly spatial distributions of major pollutants and air quality related meteorological conditions. Temporal variations of both meteorological elements and pollutant species at more than 500 stations as well as real-time evaluation results are also provided online.
\nThe anthropogenic emission used in RAEMS was yearly updated since 2016 to fit the change of emission as well as the adaptability of the modeling system. The emission was updated monthly based on that used in the same month of previous year. These adjustments were majorly depended on the results of monthly evaluation of previous year and information of emission regulation and control implementing in that month as well as the feedback from the forecasters in operational agencies who use the products every day. In the treatment, the ratio of bias median to observational average for each city was taken as the key indicator for adjusting. At the same time, performance of NO2 and SO2 and primary PM emission was most focused because of the importance of S-N-A in secondary aerosol [55, 56] and that of primary aerosol. For example, the evaluation showed that NO2 was obviously underestimated in the northern and southern parts of East China region with bias ratios of over −25% in December, 2015 (Figure 3). SO2 forecasts showed more serious underestimation for most cities in these two areas. But the RAEMS overestimated NO2 and SO2 for many cities in the middle region, especially for the cities along the Yangtze River. Therefore, the emitting intensities of NO2 and SO2 in December, 2016 were increased or decreased in different amounts separately for different areas. Accordingly, other emitting species were adjusted in the similar way. The amounts were estimated experientially based on ratios and control information.
\nThe distribution of the ratio of forecast bias median to the observational average in December, 2015 for NO2 (left) and SO2 (right).
A comprehensive evaluation on the performance of RAEMS was carried out in [38]. In that work, the performance in the beginning of two natural years of 2014 and 2015 was exhibited. They analyzed the series of statistical indicators for variables of PM2.5, ozone, PM10, NO2, SO2 and CO. The indicators included mean bias (MB), mean error (ME), root mean square error (RMSE), correlation coefficient (R), normalized mean bias (NMB) and error (NME), factor of 2 of measurement values (FAC2, the ratio of forecast records within between half and twice of measurement values), Fractional bias (FB) and error (FE), etc. Category performance with different exceedance limits was also evaluated for the two most important pollutants of PM2.5 and O3-8h. In spatial, the performance of PM2.5 and DM8H ozone for main cities and PM2.5 for provincial capital cities was shown. In temporal, the consistency of different forecast time length of PM2.5 and ozone and diurnal variation and the distribution of peak time of ozone was analyzed.
\nIn general, their results showed that the RAEMS has good performance in forecasting the temporal trend and spatial distribution of major air pollutants over eastern China region and the performance is consistent with the increasing forecast time length up to 3 days. All summarized statistical indicators of daily PM2.5 and DM8H ozone in different forecast time lengths were comparable with each other and no distinct disagreements were shown. About half of cities have correlation coefficients greater than 0.6 for PM2.5 and 0.7 for DM8H ozone. The forecasted PM2.5 concentrations were generally in good agreements with observed concentrations, with most cities having NMB within ±25%. Forecasted ozone diurnal variation was very similar to the observations and made small peak time error. The modeling system also exhibited acceptable performance for the other air pollutants. More detailed information can be found in [38].
\nHere more evaluation results were given for the city of Shanghai, one of the largest cities around the world, to show a glimpse on the continuity of forecast performance and how the forecast system performed after 2015. Figure 4 shows the scattering results of observed and 48-h forecasted PM2.5 and O3-8h for 4 years from 2014 to 2017. It shows that RAEMS had generally good performance in forecasting the two most important air pollutants. For PM2.5, the four-year average observed concentration was 46.9 μg/m3 and the forecasted concentration was only 0.1 μg/m3 overestimated. The correlation coefficient between observation and prediction of PM2.5 was 0.74. It also revealed relatively low RMSE and NMB, 22.3 μg/m3 and 8.1%, respectively and high FAC2 of 0.89. This result suggested that 89% forecasted PM2.5 concentrations were within between half and twice of those of observed. These indicators showed excellent performance in forecasting and modeling PM2.5. The NMB of 8.1% was much lower than the acceptable threshold value of ±20% recommended in the United Kingdom [57]. For example, Chen et al. reported a FAC2 of around 60% and NMB of 17 and 32% for polluted and clean periods [32]. Grell et al. reported a R2 of 0.38 for simulating PM2.5 over New Hampshire using WRF-Chem [20]. Foley et al. Reported a NMB of 19% [33]. Prank et al. found under-estimation of 10–60% over Europe using four chemical transport models of CMAQ, EMEP, LOTOS-EUROS and SILAM [58]. Wu et al. reported FAC2 of 70–80% [39]. For O3-8h, the forecasts showed better performance in indicators of correlation coefficient, NMB, and FAC2, but worse in MB and RMSE comparing with corresponding indicators for PM2.5. The performance for Shanghai has high scores among the cities over the eastern China [38].
\nScattering plot of 48-h forecasted and observed daily mean PM2.5 and O3-8h for shanghai during 2014–2017.
The performances for different years were generally consistent for both PM2.5 and O3-8h (Table 2). For example, the values of FAC2 were around 0.89 for PM2.5 and 0.93–0.97 for O3-8h, respectively. RMSEs were within 20.8–23.9 μg/m3 for PM2.5 and 28.2–32.9 μg/m3 for O3-8h, respectively. Correlation coefficients agreed well with each other. But MBs and NMBs had some difference. MBs showed that the concentration of PM2.5 was underestimated in 2014 and 2015 while overestimated in 2016 and 2017 although the biases were not very large. O3-8h was underestimated in 2015 and overestimated in the other 3 years. NMBs for PM2.5 in 2017 and for O3-8h in 2014 were relatively larger. In general, most statistical indicators for different years were comparable with each other.
\n\n | R | \nMB | \nRMSE | \nNMB (%) | \nFAC2 | \nR | \nMB | \nRMSE | \nNMB (%) | \nFAC2 | \n
---|---|---|---|---|---|---|---|---|---|---|
All | \n0.74 | \n0.1 | \n22.3 | \n8.1 | \n0.89 | \n0.80 | \n3.5 | \n30.6 | \n7.2 | \n0.95 | \n
2014 | \n0.75 | \n−0.7 | \n23.9 | \n4.4 | \n0.89 | \n0.80 | \n15.8 | \n32.0 | \n21.3 | \n0.96 | \n
2015 | \n0.78 | \n−5.6 | \n22.5 | \n−2.3 | \n0.89 | \n0.81 | \n−6.5 | \n30.0 | \n−1.4 | \n0.94 | \n
2016 | \n0.73 | \n1.3 | \n22.1 | \n13.3 | \n0.89 | \n0.76 | \n2.1 | \n32.9 | \n5.9 | \n0.93 | \n
2017 | \n0.75 | \n5.3 | \n20.8 | \n17.2 | \n0.88 | \n0.86 | \n2.8 | \n28.2 | \n3.2 | \n0.97 | \n
Summarized statistics of forecast performance of daily PM2.5 (left panel) and O3-8h (right) for different forecast length (units: μg/m3 for MB and RMSE).
To evaluate the capability of RAEMS on forecasting pollution, the categorical performance was calculated using the definition referenced in [20, 38] and the results are listed in Table 3. Only one heavy pollution for O3-8h (>265) occurred and therefore it was not included in the analysis. The exceedance limits were set using the criterion values for lightly, moderately, and heavily (PM2.5 only) polluted level in the technical regulation of CNAAQS2012. The results showed that the forecast performance decreases with increased exceedance limits for both PM2.5 and O3-8h. The values probability of detection and critical success index decrease with higher exceedance limit, while those of missed detection rate and false alarm rate increase. The biases are relatively steady and show slight over-estimation for PM2.5 and some for O3-8h. An interesting result is found for accuracy that it tends to increase with higher exceedance limits. Further analysis showed that this result is ascribed to the big percentage of the records under limits.
\nExceedance limit (μg/m3) | \n75 | \n115 | \n150 | \n160 | \n215 | \n
---|---|---|---|---|---|
Accuracy (%) | \n87.2 | \n95.0 | \n98.5 | \n91.5 | \n97.0 | \n
Probability of detection (%) | \n63.5 | \n44.1 | \n40.0 | \n75.3 | \n67.5 | \n
Missed detection rate (%) | \n36.5 | \n55.9 | \n60.0 | \n24.7 | \n32.5 | \n
False alarm rate (%) | \n43.5 | \n60.6 | \n68.4 | \n42.3 | \n52.6 | \n
Critical success index (CSI) | \n0.43 | \n0.26 | \n0.21 | \n0.49 | \n0.39 | \n
Bias | \n1.12 | \n1.12 | \n1.3 | \n1.31 | \n1.43 | \n
Categorical performance evaluated with different exceedance limits for PM2.5 (left panel) and O3-8h (right).
In general, RAEMS makes good performance on forecasting the major air pollutants over eastern China region. It also provides reliable products to support and promote the work on environmental meteorology and positive effects on increasing the ability to serve the decision-making and the public.
\nThe previous studies also showed shortage and uncertainty in several aspects in simulating and forecasting air quality using numerical models, although great improvements were achieved. The outputs of air pollutant concentrations from numerical models are more or less different from the observations in most cases. In other words, the bias of prediction and observation is usually more than 10%. If the forecast performances well, the bias could be even less than 10% (e.g. in [20, 32, 38]). For the ratio modeled value within between half and twice of observation, good performance could be around 90% in this work, while 70–80% [38, 39] or lower [58] were more recorded. Moreover, the temporal variation of model always varies from that of observation. This can be represented in correlation coefficient or ozone peak time as one often focused issue. High correlation coefficients could be greater than 0.7 or even 0.8 (in this work and [32, 38]), usually 0.5 or 0.6 (in [20, 32, 38, 39]) or lower (in [36]). A certain percentage of forecasted ozone peak time was several hours different from observed [32, 38]. The third aspect is that model performance is generally inconsistent in space, in other words, it may perform very well over some areas but poorly over some other areas in the same simulation using the same model. This phenomenon of inconsistency existed in results of all work. The models are not as satisfied in polluted situations as in usual or clean conditions while pollution always takes more attention in many regions. For example, RAEMS did not provide enough satisfied forecast for air pollution, especially heavy pollution for Shanghai shown in former sections as well as in [20, 38] which showed unsatisfied results for high ozone in US. The performance on predicting aerosol components was worse than that on the integrated mass concentration (e.g. PM2.5 and PM10) (e.g. in [19, 20, 32]). This concerns to visibility and haze related forecast, which leads to lower capability of models in forecasting visibility and haze events.
\nMajor components which caused the uncertainty on numerical air quality modeling and forecasting could be classified into the several following issues. First of all, emission inventories are important as they were always mentioned in many previous studies [21, 32, 38, 41]. Emissions can be classified into natural emissions and anthropogenic emissions. Natural emissions are from respiration and photosynthesis of plants, sea spray, forest fire, volcano explosion, etc. Many sources of deviation could be included in the model calculation because it’s impossible for modelers to know all of the details that can influence emission. For example, it is hard to obtain the fully accurate information on the growing states and types of plants, ambient conditions such as temperature, humidity, radiance, etc. in the region and duration to be forecasted or modeled. In forecasting, it is also difficult to know exactly when, where or even whether a forest fire or volcano explosion will occur or not. There are also many kinds of uncertainties in calculating the anthropogenic emissions. The inventory is always 2 or 3 years delayed and supplies the total amount of emission for 1 month or 1 year. In most situations, the diurnal variations used in the modeling are solid in time and space and cannot describe the tempo-spatial change due to actual activities of industry, traffic, etc. Another gap is that basic monitoring data is not sufficient enough for producing anthropogenic emission inventory in chemical species and spatial resolution, and therefore many approaches are implemented in developing inventories. At the same time, inventories are also sufficient enough for modeling, e.g. the number and types of chemical species and the height of each power plant.
\nThe second uncertainty came from model representation. While developing a model, scientists always endeavored to balance the scientific understanding and the goal of extremely “perfect” performance. But in fact, a perfect model is always idealized and being sought. The understanding of the chemical processes formatting or depleting air pollutants, the physical processes that transport or disperse air pollutants, the ambient conditions that influence chemical reactions is advancing. Forecast models usually introduce relatively mature technologies and keep them suitable for most situations. New technology is always developing to study or solve problems and be implemented into forecast model when it is validated. So, air quality models were in progress in the past and there is still some shortage or uncertainty in “current” model. Concerning RAEMS, its core model was developed several years ago and some elements were not included which were confirmed to influence the performance. For example, aerosol direct forcing in solar radiation was not considered in the model, which leads to more solar radiative flux to the air near ground and to ground surface. This deficiency results in higher near surface wind speed, PBL height and stronger vertical diffusion and thus lower primary pollutants and PM2.5 [21, 41]. This model missed some heterogeneous uptake of sulfate under high relative humidity conditions. For example, Wang et al. [14] and Cheng et al. [25] found a new source from reactive nitrogen chemistry in aerosol water, which explained the missing of sulfate and particle matter in extreme pollution conditions in northern China region.
\nBias may come from the treatments and inputs of initial and lateral boundary conditions. Usually, input data for initial and boundary conditions includes biases comparing with “real” atmosphere and is coarser than regional air quality model. More on this issue in meteorological predictions can be found in the other chapters and chemical aspects are analyzed here. Specific to RAEMS, the inputted meteorological data is 0.5 degree and much coarser than the model resolution of 6-km. The interval of 6-h may also involve bias in calculating the tendency of meteorological variables. The treatment of lateral boundary conditions in chemistry using historic mean field may make them far from reality. The missing of assimilation on both meteorological and chemical variables produced initial bias. The impact of such missing on air pollutants may exist in several hours since the model start over strong emitting regions but last for a long time over downwind regions, as the effect of chemical assimilation can be kept within 12–24 h [59]. Better initial chemical conditions are strongly needed for nowcasting of air quality.
\nThe uncertainty in meteorological variables could be another important source. It is known that meteorological variables are drivers of CTMs. Some of them drive the processes of advection, convection, dispersion, turbulent mixing, etc. Some of them participate in chemical reactions such as vapor or decide the reactivity rate. This chapter will not focus on this for much discussion, however, this uncertainty can be found in other chapters which concern meteorology prediction. But one point we should emphasize is that the uncertainty in forecast of weak weather conditions will be paid more attention to because heavy air pollution often occurs under such conditions, although weak conditions are not so focused in meteorology for less extreme weather occurring.
\nTo improve the performance of numerical air quality forecast, several types of work are taken into consideration in the future. As one important application of numerical meteorological prediction and the role of meteorological variables driving CTMs, introduction of better numerical forecast of weather is always one economical and effective way to improve air quality forecast. This way should be carried out indubitably if it is feasible in technology.
\nUpdate in emission inventory and its implementation in CTMs is another core action. It includes several aspects: (1) reduction of time delay; (2) increase in horizontal and vertical resolution; (3) improving the accuracy of emission inventory itself; and (4) improving the applicability in models. The former three aspects mainly require efforts of inventory community and the last one needs efforts of modelers. Specifically, one job is to improve on-line calculated emissions, such as biogenic volatile organic compounds. For example, biogenic emissions can be calculated using model meteorological variables and some inputted static data in many current CTMs (e.g. WRF-Chem and CMAQ). Better vegetation data (classification, leaf area, etc.) will benefit improvements of biogenic emissions and they can be retrieved from satellite data nearly real time. The other is to build one fast technology to adjust the emission data inputted into forecast system. The determination of indicators which may be used to adjust the emission data will be the first step and then develop a relatively fast evaluation system or technology to supply the result how the forecast performed in previous duration. Based on the evaluation results, a fast adjustment technology is to be implemented to update emissions used in the coming forecasts. Besides the regular treatments, fast response to emergency or temporary emission control needs to be prepared based on relatively less detailed information.
\nTo fit the extending needs, numerical air quality forecast is increasing its capability on longer predictable period, finer resolution, and better service for other interests. Long time length and fine scale is the two main aims or requirements of coming air quality prediction besides higher accuracy. Long prediction of over 1 week has been urgently needed and required by decision-making agencies during recent years. Under the strong requirements on improving air quality, environment protection agencies over eastern China often carry out or be demanded to carry out temporary emission control to reduce air pollution. This action usually needs a few days ahead of predicted pollution episode. Another important need is on macro-management of industrial production, electric power, etc. for long-term objectives such as the level of annual mean air pollutant concentrations and the level of days of pollution. It requires climate scale prediction of air quality, such as monthly or seasonally. The other aim is finer forecast in space and time. For example, tasks of air quality forecast for a specific community or a specific time point were required, which were far beyond the capability of current forecast service 3 times a day for the entire Shanghai. Many other interests, such as human health service, also need the support of numerical air quality forecast. These needs require supporting information beyond forecast results to promote their own goals.
\nComparing with that in meteorological prediction, treatment and approach in initial and boundary layer conditions is rough and ongoing. Assimilation on air quality related variables or chemical assimilation is needed to improve initial conditions and forecast performance, especially in nowcasting of air quality. Of course, chemical assimilation is more difficult than meteorological assimilation due to insufficient monitoring data. Implementation of real time global forecast in boundary is another way to reduce bias from lateral input out of the model domain. This treatment will greatly benefit the forecasts near model lateral boundary and of long-term period.
\nImprovement in representation of CTMs such as involving the feedback and interaction between meteorological variables and air pollutants is one persistent work. This work will provide better models for numerical air quality forecast and is essential for improving model performance. But it depends on scientific understanding and technological maturity. Some nowaday jobs could focus on increasing model performance on near-surface wind, vertical diffusion of particles, aerosol species, and diurnal variation in operational forecast. We should show more desire to involve new technology into forecast system in the future.
\nAir pollution is focused because of its adverse effects, e.g. on human health. Numerous works were taken into action including scientific study, policy making, and emission control, etc. over eastern China due to the severe situation as one of the most polluted areas. This chapter illustrated the numerical forecast of air quality over the eastern China region, especially what has been done in Shanghai Meteorological Service.
\nNumerical air quality forecast has become truly profiting from the achievements on air quality models and computation technology during past decades. Three-dimensional chemical transport models were the major choice in studying and predicting air quality in both global and regional scale after entering the twenty-first century. In very recent years, online approach CTMs, which calculate meteorological and chemical variables in one model, prevent from the loss of information between two meteorological outputs, and benefit involving the interaction between meteorology and air pollution. The fully online coupled WRF-Chem was chosen to develop the Regional Atmospheric Environmental Modeling System for eastern China by SMS for its good performance in modeling the air quality/pollution over this region.
\nThe operational RAEMS was certified by China Meteorological Administration in March 2013 and has been providing numerical forecast data and products from then on. This forecasts greatly promoted the air quality prediction, air pollution warning, and decision-making service in meteorological agencies as well as environmental protection agencies. A previous detailed evaluation validated the performance on forecasting the spatial distribution and temporal variation of major air pollutants over eastern China region during the 2 years of 2014–2015 [38]. For the two most important air pollutants of PM2.5 and O3-8h for the city of Shanghai, RAEMS had excellent performance during 2014–2017 as analyzed in this chapter. At the same time, RAEMS showed relatively lower accuracy under polluted conditions than unpolluted conditions, and it even performed worse under heavier polluted conditions.
\nFurther analysis showed that shortage or uncertainty in current numerical air quality forecast mainly came from four aspects of emission inventory or emission related inputs, model capability in chemical representation, biases in initial and lateral boundary layer conditions, and uncertainty in meteorological variables. These suggested ideas for improving performance of forecasts in the future. Longer predictable period and finer temporal and spatial resolution is also important goal and challenge for fitting the extending needs from application communities.
\nThe development of RAEMS was a joint work in Shanghai Meteorological Service and collaborated with many colleagues such as Jianming Xu, Fuhai Geng, Li Peng, Ying Xie, etc. and guided by many experts e.g. Xuexi Tie, Greg Carmichael, and Georg Grell, etc. This work was sponsored by the National Key R&D Program of China (Grant nos. 2016YFC0201900 and 2016YFC0203400).
\nThe authors declared that they have no conflicts of interest to this work.
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