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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. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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1. Introduction
Although around 80% of men aged 80 years and older and 15% to 30% of men aged 50 years and older have microscopic undiagnosed prostate cancer found at autopsy, only 3% men die because of prostate cancer.1 Increasing prostate-specific antigen (PSA) screening at younger ages has increased overdiagnosis2 and overtreatment3 of localized prostate cancer (LPC). More than 90% of US patients currently diagnosed with prostate cancer have LPC and approximately 94% of patients with LPC choose treatment.4 Based on data from leading studies, a model had recently projected only a 0% to 2% 15-year mortality from low-grade (Gleason score <7) screen-detected LPC in men aged 55 to 74 years if they chose observation instead oftreatment.5 By consensus, urologists and radiation oncologists recommend treatment for LPC if a patient has a further 10-year life expectancy 6 (the10-year rule7) regardless of cancer grade, even though no randomized trials have shown that treatment can improve survival in patients in whom the cancer was screen-detected. National guidelines by the American Cancer Society and the National Comprehensive Cancer Network (NCCN) also recommend treatment for most patients.8 However, in the review by Zeliadt et al,9 different studies had found that patients rate the sexual, urinary, and bowel side effects of treatment to be just as important as the potential benefit in survival; that if risks and benefits of treatment were explained with-out bias, 75% of patients chose a lower radiation dose despite a lower predicted survival; that 90% of physicians but fewer than 20% of patients ranked the effect of treatment on survival as one of their top 4 concerns; and that patients who chose treatment believed that treatment was guaranteed to improve survival. At a median of 6 years after treatment, health-related quality of life (HRQOL) of treated patients was worse than that of controlpatients.10 Many patients regretted that they chosetreatment.11
To our knowledge, no studies of patient-physician communication have examined patients’ anticipated survival benefit of treatment. Without data from randomized trials in screen-detected patients, it is difficult to counsel patients regarding their survival with and without treatment. Even with the use of multi-factorial models, accuracy of predicted survival is 75% or lower.12 Physicians are also poor at estimating baseline co-morbidity adjusted life expectancy (CALE), which is critical in making an informed decision.13 Thus, patients may accept a treatment recommendation not knowing what their baseline CALE is, how much the newly diagnosed cancer could reduce it, or how effectively treatment could minimize that reduction. Current over-treatment of LPC might be because patients do not understand the pros and cons of treatment.
In this study we surveyed newly diagnosed patients about their anticipation of survival with and without treatment. By estimating their baseline CALE without considering the newly diagnosed cancer, we calculated their perceived decrease in longevity with observation (PDLO), and their perceived increase in longevity with treatment (PILT) for the cancer.
2. Methods
We surveyed patients who had been newly diagnosed with LPC (stages T1a to T2c) in the preceding 6 months, had met with their urologist after the diagnosis, were scheduled to receive treatment or observation, and had not yet been treated with surgery or radiation. Patients with dementia, or those who could not read, write, or understand English, were excluded. All patients were recruited from a large, private urology practice in Norfolk, Virginia. Staff at this practice systematically contacted patients newly diagnosed with LPC between March 2005 and November 2007 regarding their interest in participation in a self-administered mailed survey. Two concomitant pretreatment self-administered surveys were used. The first survey asked patients about expectations of survival with and without treatment, co-morbid diseases, mood, social support, satisfaction with life, health, and education by physicians about treatment options. A list of health-related words in a closed envelope was mailed with the survey. Patients were requested to open the envelope and read these words on the telephone to a research assistant. This was done to estimate patient health literacy by using a brief version of the Rapid Estimation of Health Literacy in Medicine scale.14 Patients were given a $10 stipend for completing this survey. A second pretreatment survey was a part of a longitudinal follow-up by urologists to evaluate generic HRQOL, prostate cancer related symptoms, and fear of cancer recurrence. The study methods were reviewed and approved by an Institutional Review Board.
3. Measures
The Charlson Comorbidity Index (CCI) is a validated measure of co-morbidity. We used a patient self-reported CCI scale that asked about the presence and severity of 12 chronic conditions; the Prostate Cancer Outcomes Study used this CCI version.15 Score categories are 0, 1, 2, and 3 or more diseases.
The NCCN practice guidelines had recommended, for the first time in 2007,8 that the health adjusted life expectancy of LPC patients can be estimated by weighting age-based life expectancy by 1.5 for patients in the highest health quartile, using no weighting for patients in the middle 2 health quartiles, and weighting by 0.5 for patients in the lowest health quartile. We used co-morbidity scores as surrogate markers of health status because co-morbidity is the main determinant of life expectancy in older patients, 16 and the most important prognostic factor for patients with LPC who are <75 years old is the co-morbidity score. 17 We categorized patients into health quartiles by using their CCI score (0 disease score = highest health quartile; 1 or 2 disease score = middle 2 health quartiles; 3 or higher disease score = lowest health quartile). Our basis of equating a 1 or 2 disease score with the middle 2 health quartiles was that almost half (49%) of the 3173 patients newly diagnosed with LPC in a Prostate Cancer Outcomes Study15 had a disease score of 1 or 2, and almost half (55.5%) of our patients had a disease score of 1 or 2. Both studies used the same version and scoring of the CCI. Patients were placed in 4 CALE categories: <5 years, 5 to 10 years, 10 to 20 years, and 20 years. These 4 categories were scored, respectively, from 1 to 4.
Patients were asked the following 2 questions: “How long do you expect you will live without any treatment for prostate cancer?” (Q1) and “How long do you expect you will live after treatment for prostate cancer?” (Q2). The possible responses to both questions were grouped into 4 categories (similar to the CALE categories): <5 years, 5 to 10 years, 10 to 20 years, and >20 years. These 4 categories were also scored from 1 to 4, respectively. Based on Q1, Q2, and CALE scores, we calculated the patient’s PDLO (which is CALE category score minus Q1 category score) and the patient’s PILT (which is Q2 category score minus Q1 category score). A PDLO of 10 or more years is at least a 2-category difference between the CALE category and the Q1 category; this is only possible if the CALE was >20 years and the Q1 response was 5 to 10 years or if the CALE was 10 to 20 years and the Q1 response was <5 years. Similarly, a PILT of 10 or more years indicates that the response to Q2 was 10 years or more than the response to Q1. We conducted ordinal logistic regression analyses to identify the main socio-demographic, health, and cancer characteristics that could predict PDLO and PILT of 10 or more years.
The following validated self-administered scales were used. (1) The Short-Form 36 (SF-36, version 2) measures generic HRQOL; we calculated physical component summary and mental component summary scores from SF-36 data.18 (2) The Prostate Cancer Index measures urinary, sexual, and bowel symptoms and how much they bother the patient.19 (3) The Duke Activity Status Index20 measures functional capacity in metabolic equivalents; this scale asks patients whether they could perform 12 activities which have different levels of exertion. (4) The Hospital Anxiety and Depression Scale measures the presence and severity of anxiety and depression.21 (5) The Fear of Cancer Recurrence Scale measures the fear of possible cancer recurrence.22 (6) The Medical Outcomes Study Social Support Survey measures social support in an overall score that includes multidimensional sub-scores. 23 (7) The Delighted-Terrible Seven Faces Scale24 was used to measure patient satisfaction with life, health, and with education given by physicians about treatment options for LPC. (8) The Rapid Estimation of Health Literacy in Medicine scale, discussed earlier, measures health literacy; this was the only scale administered by telephone.14
4. Statistical analyses
Frequencies and relative frequencies were used to describe categorical variables. Continuous variables were described using the mean, median, and SD. Chi-square tests, Fisher’s exact test, and independent sample t tests were used to examine bivariate associations. Unadjusted and adjusted odds ratios (ORs) and their 95% CIs were estimated using ordinal logistic regression analysis. Socio-demographic and health factors that were found to be associated with PDLO and PILT in the bivariate analysis at an alpha level of 0.20 were kept in the multivariate models. All analyses were performed using SAS software (version 9.1, SAS Institute, Inc., Cary, NC).
5. Results
Surveys were mailed to 430 patients newly diagnosed with LPC, but 69 patients had already started treatment by the time the patients received the surveys, 3 patients never received the surveys, and 2 patients were found to be ineligible to participate because their cancer was not localized to the prostate. Of the 356 remaining patients, 104 patients did not return the survey because they were “not interested” in participating and 68 patients who did not return the surveys did not give a reason for not participating or could not be contacted. One hundred eighty-four of 356 patients (survey response rate of 52%) completed and returned the first pretreatment survey; 23 of these 184 patients (12.5%) patients chose observation. Table 1 shows a demographic comparison of patients who chose treatment or observation. Mean patient age was 61.5 years, and most patients reported college education and a family income of >$50,000.
Overall
Treatment
Observation
Characteristic
(n [%])
(n [%])
(n [%])
P‡
Age (years)
n = 184
n = 161
n = 23
<60
71 (38.6)
68 (42.2)
3 (13.0)
60–70
91 (49.5)
81 (50.3)
10 (43.5)
"/>70
22 (11.9)
12 (7.5)
10 (43.5)
<.0001
Mean +/- SD
61.5 +/- 7.9
60.6 +/- 7.6
68.2 +/- 5.9
<.0001
Race
n = 184
n = 161
n = 23
African American
26 (14.1)
26 (16.2)
0 (0.0)
White
158 (85.9)
135 (83.9)
23 (100)
.05§
Education
n = 180
n = 157
n = 23
<High school
7 (3.9)
7 (4.5)
0 (0.0)
High school
65 (36.1)
56 (35.7)
9 (39.1)
College
108 (60.0)
94 (59.9)
14 (60.9)
.58
Health literacy
n = 173
n = 150
n = 23
Below 6th grade
1 (0.6)
1 (0.7)
0 (0.0)
6th-9th grade
16 (9.3)
15 (10.0)
1 (4.4)
"/>9th grade
156 (90.2)
134 (89.3)
22 (95.7)
.63
Family income
n = 179
n = 156
n = 23
Low ("/>$50,000)
57 (31.8)
50 (32.0)
7 (30.4)
High (="/>$50,000)
122 (68.2)
106 (67.9)
16 (69.6)
.88
Table 1.
Comparison of Socio-demographic Characteristics of Patients with Localized Prostate Cancer who Chose Treatment* and Observation†. *Treatment patients had either surgery or radiotherapy. †Observation patients had neither surgery nor radiotherapy. ‡Unless otherwise specified, statistical significance is for chi-square test (categorical variables) or independent samples t test (continuous variables). §Fischer’s exact test.
Table 2 shows a comparison of patients who chose treatment or observation by cancer grade, PSA, life expectancy by age, and co-morbidity scores. Mean Gleason grade was 6.6. Table 2 also includes a comparison of these patient groups by SF-36 scores (Physical Component and Mental Component Scores), as well as the urinary, sexual, and bowel function scores of the 144 of 184 patients who had also returned the second pretreatment survey. Table 3 shows a comparison of patients who chose treatment or observation by CALE, anxiety and depression, function capacity, social support, and satisfaction. Mean CALE was 22.9 years. Table 4 shows the baseline CALE for the 184 patients and the responses of 170 of the 184 patients who had answered questions about their perceived life expectancy without treatment (Q1) and with treatment (Q2) of the cancer. Without treatment, perceived life expectancy was <5 years in 15.2%, 5 to 10 years in 48.8%, 11 to 19 years in 33.5% and 20 or more years in 2.4% of the patients. With treatment, it was <5 years in 0.6%, 5 to 10 years in 6.5%, 11 to 19 years in 30.0%, and 20 or more years in 62.9% of the patients. By contrast, baseline CALE was <5 years in 0.5%, 5 to 10 years in 2.2%, 11 to 19 years in 36.4%, and 20 or more years in 60.9%. A total of 170 patients had data on CALE, Q1, and Q2. As compared with CALE, 65 (38.2%) of these 170 patients expected their survival to decrease by 10 or more years without treatment. As compared with their perceived survival without treatment, 81 (47.6%) of 170 patients expected their survival to increase by 10 or more years with treatment. Of the 108 patients with a baseline CALE of >20 years, only 2 (1.9%) expected to live beyond 20 years without treatment whereas 84 (77.8%) expected to live beyond 20 years with treatment (data not shown). Neither of these perceptions was significantly related to whether the patients chose treatment or observation. However, to a statistically insignificant extent, patients who chose treatment were more likely than observation patients to expect a 10 or more years reduction in survival without treatment (50% vs. 33.3%, _2 test; P =.26) and a 10 or more year increase in survival with treatment (39.9% vs. 16.7%, Fisher’s exact test; P =.13).
Overall
Treatment
Observation
Factors
(n [%])
(n [%])
(n [%])
P*
Gleason Grade
n = 184
n = 161
N = 23
2–4
0 (0.0)
0 (0.0)
0 (0.0)
5–6
103 (55.9)
84 (52.1)
19 (82.6)
7
62 (33.7)
60 (37.3)
2 (8.7)
8–10
19 (10.3)
17 (10.6)
2 (8.7)
0.02
Mean +/- SD
6.6 +/- 0.7
6.6 +/- 0.7
6.2 +/- 0.7
0.02
Prostate-specific antigen
n = 183
n = 161
n = 23
=<10
159 (86.9)
139 (86.3)
21 (91.3)
"/>10
24 (13.1)
22 (13.7)
2 (8.7)
0.74
Mean +/- SD
6.7 +/- 5.3
6.8 +/- 5.5
5.6 +/- 3.4
0.14
Life expectancy by age (years)
n = 183
n = 160
N = 23
<10
4 (2.2)
2 (1.3)
2 (8.7)
10–20
104 (56.8)
86 (53.8)
18 (78.3)
"/>=20
75 (40.9)
72 (45.0)
3 (13.0)
0.002*
Comorbidity score
n = 184
n = 161
N = 23
0
74 (40.2)
66 (40.9)
8 (34.8)
1
75 (40.8)
67 (41.6)
8 (34.8)
2
27 (14.7)
23 (14.3)
4 (17.4)
"/>=3
8 (4.4)
5 (3.1)
3 (13.0)
0.16
Mean +/- SD
0.9 +/- 1.0
0.83 +/- 0.96
1.26 +/- 1.5
0.18
Short Form-36 subscales
Physical component summary
n = 142
n = 134
n = 8
Mean +/- SD
54.5 +/- 7.6
54.5 +/- 7.2
52.6 +/- 12.3
0.66
Mental component summary
n = 142
n = 134
n = 8
Mean+/- SD
44.1 +/-6.8
43.9 +/- 6.9
46.4 +/- 4.5
0.29
Prostate cancer index
Urinary
n = 141
n = 132
n = 9
Mean +/- SD
90.2 +/- 16.9
90.1 +/- 17.4
91.3 +/- 9.3
0.83
Bowel
n = 142
n = 133
n = 9
Mean +/- SD
88.8 +/- 12.5
88.5 +/- 12.8
92.9 +/- 4.5
0.03
Sexual
n = 137
n = 128
n = 9
Mean +/- SD
57.7 +/- 29.9
57.8 +/-30.4
55.4 +/- 23.8
0.82
Urinary Bother
n = 144
n = 135
n = 9
Mean +/- SD
86.1 +/- 23.0
85.9 +/- 23.4
88.9 +/- 18.2
0.71
Bowel Bother
n = 144
n = 135
n = 9
Mean +/- SD
92.5 +/- 17.5
92.0 +/- 17.9
100 +/- 0.0
0.0001
Sexual Bother
n = 142
n = 133
n = 9
Mean +/- SD
65.8 +/- 36.8
65.4 +/- 36.8
72.2 +/- 38.4
0.59
Fear of cancer recurrence
n = 141
n = 133
n = 8
Mean +/- SD
10.7 +/- 3.8
10.7 +/- 3.7
10.9 +/- 4.5
0.92
Table 2.
Comparison of Patients who Chose Treatment and Observation by Prostate Cancer-Related and Health Factors
Overall
Treatment
Observation
Characteristic
(n [%]
(n [%]* **
(n [%]† ***
P‡ *
Comorbidity-adjusted life expectancy
All ages
184 (100)
161 (100)
23 (100)
<5 years
1 (0.5)
0 (0)
1 (4.3)
5–10 years
4 (2.2)
2 (1.2)
2 (8.7)
11–19 years
67 (36.4)
52 (32.3)
15 (65.2)
"/>=20 years
112 (60.9)
107 (66.5)
5 (21.7)
<.0001
Mean +/- SD
22.9 +/- 7.6
23.9 +/- 7.3
<
.0001
Anxiety score
n = 183
n = 160
n = 23
None/normal (0–7)
145 (79.2)
123 (76.9)
22 (95.7)
Mild anxiety (8–10)
23 (12.6)
23 (14.4)
0 (0.0)
Moderate anxiety (11–14)
12 (6.6)
11 (6.9)
1 (4.4)
Clinical (15–21)
3 (1.6)
3 (1.9)
0 (0.0)
.18
Mean +/- SD
5.1 +/- 3.5
5.4 +/- 3.5
2.8 +/- 2.9
.0008
Depression score
n = 178
n = 156
n = 22
None/normal (0–7)
172 (96.6)
150 (96.2)
22 (100)
Mild depression (8–10)
4 (2.3)
4 (2.6)
0 (0.0)
Moderate (11–14)
1 (0.6)
1 (0.6)
0 (0.0)
Clinical (15–21)
1 (0.6)
1 (0.6)
0 (0.0)
.83
Mean +/- SD
1.7 +/- 2.3
1.7 +/- 2.4
1.4 +/- 1.7
.49
Functional capacity
n = 184
n = 160
n = 23
Mild activities (<3 METs)
1 (0.5)
1 (0.6)
0 (0.0)
Moderate activities (3–6 METs)
20 (10.9)
15 (9.3)
5 (21.7)
Vigorous activities ("/>=6 METs)
163 (88.6)
145 (90.1)
18 (78.3)
.19
Social support
n = 183
n = 160
n = 23
<50
7 (3.8)
6 (3.8)
1 (4.4)
50–75
33 (18.0)
29 (18.1)
4 (17.4)
75–100
143 (78.1)
125 (78.1)
18 (78.3)
.98
Satisfaction with Life
n = 178
n = 156
n = 22
Delighted or highly satisfied
138 (77.5)
121 (77.6)
17 (77.3)
Satisfied or lower
40 (22.5)
35 (22.4)
5 (22.7)
.97
Mean +/- SD
6.0 +/- 0.9
5.9 +/- 0.9
6.1 +/- 1.0
.64
Satisfaction with health
n = 178
n = 156
n = 22
Delighted or highly satisfied
85 (47.8)
72 (46.1)
13 (59.1)
Satisfied or lower
93 (52.3)
84 (53.9)
9 (40.9)
.26
Mean +/- SD
5.0 +/- 1.3
5.0 +/- 1.3
5.4 +/- 1.4
.19
Satisfaction with education by physician in treatment choices
n = 178
n = 156
n = 22
Delighted or highly satisfied
141 (79.2)
126 (80.8)
15 (68.2)
Satisfied or lower
37 (20.8)
30 (19.2)
7 (31.8)
.17
Mean +/- SD
6.1 +/- 0.9
6.1 +/- 0.9
5.9 +/- 1.1
.1
Table 3.
Distribution of Men by Prostate Cancer-Related and Health Characteristics. *Treatment patients had either surgery or radiotherapy. †Observation patients had neither surgery nor radiotherapy. ‡Unless otherwise specified, statistical significance is for chi-square test (categorical variables) or independent samples t test (continuous variables). MET, metabolic equivalent.
Expected Survival (years)
Baseline CALE Score
Q1
Q2
(n = 184)
(n = 170)
(n = 170)
<5
1 (0.5)
26 (15.2)
1 (0.6)
5–10
4 (2.2)
83 (48.8)
11 (6.5)
11–19
67 (36.4)
57 (33.5)
51 (30.0)
"/>=20
112 (60.9)
4 (2.4)
107 (62.9)
Table 4.
Distribution of Men with Localized Prostate Cancer by Calculated Co-morbidity Adjusted Life Expectancy. Data provided as n (%). CALE, co-morbidity adjusted life expectancy; Q1, How long do you expect you will live without any treatment for prostate cancer?; Q2, How long do you expect you will live after the treatment of your choice for prostate cancer?
Tables 5 and 6 present ordinal logistic regression models for PDLO and PILT. Age, CALE, depression, and anxiety scores predicted both PDLO and PILT. Furthermore, PSA level predicted PDLO, whereas social support predicted PILT.
Unadjusted Effects
Adjusted Effects (OR 95% CI)
PDLO =<0*
PDLO = 1†
PDLO ="/>2‡
OR (95% CI)
Age
25
62
83
1.08 (1.04–1.13)
1.01 (0.92–1.10)
PSA level
25
62
82
0.94 (0.88–1.00)
0.93 (0.86–1.00)
CALE
25
62
83
0.92 (0.88–0.96)
0.93 (0.84–1.02)
Anxiety score
25
62
83
0.88 (0.81–0.96)
0.96 (0.85–1.08)
Depression score
25
58
82
0.79 (0.67–0.93)
0.85 (0.69–1.04)
Table 5.
Ordinal Logistic Regression Modeling for Perceived Decrease in Longevity with Observation (PDLO) among Men with Localized Prostate Cancer. *A (PDLO) =<0 (reference group) indicates that CALE and self-reported survival expectation without treatment are within the same range or CALE is less. †A (PDLO) = 1 suggests that CALE exceeds self-reported survival expectation without treatment by one response category.‡A (PDLO) >=2 suggests that CALE exceeds self-reported survival expectation without treatment by at least 2 response categories (ie.,about 10 years). All covariates in the ordinal logistic regression model are defined as continuous variables.PDLO, perceived decrease in longevity with observation (categorized); PSA, prostate-specific antigen; CALE, comorbidity adjusted life expectancy; OR, odds ratio.
Unadjusted Effects
Adjusted Effects (OR 95% CI)
PILT =<0*
PILT = 1†
PILT ="/>2‡
OR (95% CI)
Age
18
87
65
1.08 (1.04–1.13)
1.09 (0.99–1.19)
CALE
18
87
65
0.93 (0.89–0.97)
1.02 (0.93–1.12)
Anxiety Score
18
87
65
0.89 (0.82–0.98)
0.91 (0.81–1.02)
Depression score
18
83
64
0.93 (0.82–1.05)
1.06 (0.89–1.26)
Social Support
18
87
65
1.01 (0.99–1.03)
1.00 (0.98–1.02)
Table 6.
Ordinal Logistic Regression Modeling for Perceived Increase in Longevity with Treatment (PILT) among Men with Localized Prostate Cancer. *A (PDLO) =<0 (reference group) indicates that CALE and self-reported survival expectation without treatment are within the same range or CALE is less. †A (PDLO) = 1 suggests that CALE exceeds self-reported survival expectation without treatment by one response category.‡A (PDLO) >=2 suggests that CALE exceeds self-reported survival expectation without treatment by at least 2 response categories (ie., about 10 years). All covariates in the ordinal logistic regression model are defined as continuous variables.PDLO, perceived decrease in longevity with observation (categorized); PSA, prostate-specific antigen; CALE, comorbidity adjusted life expectancy; OR, odds ratio.
6. Discussion
Prostate cancer is the most common solid cancer in men. Younger patients make up a fast growing population that is being screen-detected and treated for low-risk LPC.25 To our knowledge, this is the first study to report the perceptions of newly diagnosed patients about how the cancer or its treatment could affect their survival. The mean age of our patients (61.5 years) was similar to the range of 58 to 64 years of US patients currently undergoing radical prostatectomy.26 The mean Gleason grade was 6.6 in our patients, similar to other series in which almost half of screen-detected cancers were “insignificant.”27 By choosing treatment, these low-risk patients had accepted the treatment side effects in exchange for longer anticipated survival. Our questions were designed to find how much longer these patients expected to live by choosing treatment. These expectations were evaluated after the patients had discussed their treatment options with their urologists. Despite their mean baseline CALE of 22.9 years, without treatment, 26 of 170 patients expected to live <5 years and only 4 expected to live >20 years; with treatment, only 1 expected to live <5 years and 107 patients expected to live >20 years.
What should these patients really be expecting? Nearly 86% of all patients diagnosed through PSA screening are not expected to die because of prostate cancer.28 The Connecticut Tumor Registry found that almost 20% of patients with Gleason grade 6 or higher who chose observation died as a result of LPC during a period of 20 years.29 However, all the Registry’s patients had been clinically diagnosed; in contrast, patients diagnosed with screen-detected LPC are expected to have a longer survival because of a gain in lead time. Patients in another commonly cited natural history study30 were also not diagnosed by PSA screening. A review31 found that only one randomized, high-quality trial32 could find a survival benefit of treatment, but in this trial 95% of patients had cancer that was clinically palpable (and not detected by PSA screening), putting this cohort in an intermediate- to high-risk category. A study of 44,630 men found a survival benefit of treatment, 33 but only 2.1% of the patient sample had died of prostate cancer. If adjusted for lead time provided by screening and also for the impairment of HRQOL that follows treatment, treatment was projected to enhance quality-adjusted survival by only 0.5 year. 34\n\t\t\t
Overtreatment would be expected if patients believe that treatment will lead to a much longer survival. Many studies have found that nearly every patient initially wants eradication of the cancer. 9 In qualitative studies, some patients accepted side effects for any gain in survival but they were convinced that treatment would improve survival.9 Assuming that tumors would grow exponentially, urologists at the Mayo Clinic were also of the opinion that only 0.3% and 14.5% of screen-detected LPCs were “clinically insignificant.”35 Patient anxiety caused by the new diagnosis of cancer and the consensus advice of specialists that LPC patients with a CALE of >10 years should choose treatment or be offered treatment6 will lead to high treatment rates. In 70% to 90% of patients, a treatment plan is usually made in a single visit to the urologist after a positive biopsy.36\n\t\t\t
The mean Gleason grade of our patients was 6.6, and in 87% of patients the mean PSA was <10, both of which are low-risk categories but for which national guidelines recommend either observation or treatment.8 Only 12.5% of patients in our study chose observation. Specialists frequently recommend treatment even in low-risk patients because over 10 to 15 years the cancer may progress.29 To manage this risk, a strategy of active surveillance with deferred initial treatment28 is being increasingly recommended for patients at lower risk, ie, with cancers of Gleason grade <7, cancer stages T1c to T2a, and PSA <10. Almost half of patients with screen-detected cancer possess such characteristics. 27 In conjunction with specialists, primary care physicians (PCPs) can follow patients who choose this strategy. PCPs may also offer more balanced advice because they might be more knowledgeable about the patient’s preferences, co-morbidities, and baseline CALE.37 Patients with LPC may also want to review educational materials with their PCP. The American Cancer Society website was recommended because its content, accuracy, balance, and readability was rated the highest among 44 patient education materials about LPC.38
Overtreatment can also be reduced with decreased screening, and several studies have shown that fewer patients want PSA screening if they are counseled before screening.39 Enthusiasm for routine screening is high among specialists who treat LPC. In a random nationwide survey, 43% of 559 radiation oncologists recommended routine PSA screening in average-risk patients older than 80.6 Primary care physicians who frequently order PSA testing without much discussion about risks and benefits of testing cited reasons of lack of time, competing demands, limited patient health literacy, and fear of liability.40 Prescreening counseling is difficult because it is unclear what and how much discussion should occur. As yet, we cannot say that screening or treatment can improve survival. The deleterious effects of treatment on urinary, bowel, and sexual dysfunction are better known; however, their frequencies and severities after different treatment techniques have been reported in more than 800 publications,41 vary greatly, and are difficult to balance. We can also share with patients that there is a small risk of immediate morbidity and mortality associated with prostate biopsy and cancer treatment; that a new anxiety results from a positive PSA test, whether or not it is followed by a negative biopsy; and that we cannot compare the risk of death caused by co-morbid diseases with that of death caused by cancer without knowing the grade and stage of the cancer. However, patients must also know that, even if diagnosed with cancer, no randomized controlled trial has shown that treatment can or cannot improve survival in patients with screen-detected cancer. Interestingly, a study found that more African American patients wanted PSA screening after the use of a decision aid, 39 presumably because of the appreciation of the higher risk in African American patients.
In our patients, age, CALE, depression, and anxiety were the most important predictors of PDLO or PILT. Though PSA level also influenced PDLO, PILT was related to social support. PDLO and PILT were not related to other factors such as race; income; education; health literacy; physical and mental summary SF-36 scores; urinary, bowel, and sexual symptoms; choice of treatment or observation; fear of cancer recurrence; functional capacity; and satisfaction with life, health, or with education by physicians in cancer treatment options. Although the association of PDLO and PILT with continuous depression scores was statistically significant, the importance of this finding is unclear given that 96.6% patients had a depression score of <7, which indicates no depression.
Our findings may be difficult to generalize because our study sample was small. However, the differences we found between CALE and patient expectations of survival with and without treatment were large. Also, the mean age26 and mean cancer grade27 of patients newly diagnosed with LPC in large series and in our patients were similar. Our patients were treated by urologists in a private practice and more than 80% of urologists in the United States are in private practice.42 Our method of equating CCI scores with NCCN recommended health quartiles to estimate CALE is new and has not been previously validated. We used this method because we could not find any other validated method to estimate long-term health-adjusted life expectancy in individual ambulatory patients. Finally, we lost accuracy in the estimation of PDLO and PILT by asking patients to predict their survival in ordinal intervals rather than in a discrete number of years. We used ordinal intervals because it might be easier for patients to predict their survival this way, and the intervals allowed an estimation of PDLO and PILT of more than or less than 10 years. We have also published these findings earlier. 43
We had also studied whether our patients had adequate Knowledge, Understanding and Judgment (KUJ) of their treatment options by using a KUJ 18-item questionnaire that we have developed; we found that although the vast majority of our patients were educated, had good health literacy and had higher income, over half of the patients incorrectly answered over half of the questions on the KUJ scale. These findings have been published separately.44 Additionally, we had studied whether our patients had chosen treatment or observation in accordance with current NCCN guidelines and we had found that a majority had chosen over-treatment, i.e., they had chosen treatment even though for their clinical situation the NCCN had recommended observation as an equal alternative. These findings were published recently, 45 and had demonstrated that with the use of our method to estimate CALE it becomes feasible to use NCCN guidelines in decision-making in individual patients. Based on our research and NCCN guidelines, in August 2011 we have published a comprehensive and easy-to-understand approach in the journal American Family Physician (AFP)46 which can be used by newly-diagnosed patients and their physicians in quickly reaching an evidence-based and guideline-driven treatment choice. Decision-making is very hard especially for low-risk patients, traditionally primary care physicians are not involved in this process, and over 70% to 80% patients choose a treatment or observation in the first visit to the urologist after a positive biopsy. Although newer guidelines now recommend against PSA screening,47 this recommendation carries the risk of increasing mortality due to prostate cancer and prostate cancer is already the second most common cause of cancer death in American men. The algorithm and tools provided in our publication in AFP can empower primary care physicians in counseling newly-diagnosed patients and reduce the risk of over-treatment by convincing low-risk patients to choose active surveillance. With this safeguard, patients and physicians can choose screening for prostate cancer with less hesitation.
7. Conclusion
In patients with newly diagnosed LPC, in whom the mean cancer grade was <7 and in whom education, income, and health literacy was intermediate to high, almost 38% of our patients had expectations of a reduction in survival of 10 or more years if they chose observation, and 48.8% patients expected an improvement in survival of 10 or more years through choosing treatment. These expectations are highly unrealistic because no study has shown that the cancer or its treatment can affect survival by even 1 year, especially in screen-detected patients with a cancer Gleason grade of <7.
Acknowledgments
This article has been reproduced by permission of the American Board of Family Medicine. We thank Mr. Brian Main, Department of Urology, Eastern Virginia Medical School, for help with data entry and analysis.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/26748.pdf",chapterXML:"https://mts.intechopen.com/source/xml/26748.xml",downloadPdfUrl:"/chapter/pdf-download/26748",previewPdfUrl:"/chapter/pdf-preview/26748",totalDownloads:989,totalViews:100,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,dateSubmitted:"September 27th 2011",dateReviewed:"November 9th 2011",datePrePublished:null,datePublished:"January 27th 2012",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/26748",risUrl:"/chapter/ris/26748",book:{slug:"topics-in-cancer-survivorship"},signatures:"Ravinder Mohan, Hind Beydoun, Myra L. Barnes-Ely, LaShonda Lee, John W. Davis, Raymond Lance and Paul Schellhammer",authors:[{id:"58197",title:"Prof.",name:"Ravinder",middleName:null,surname:"Mohan",fullName:"Ravinder Mohan",slug:"ravinder-mohan",email:"rmkk20@yahoo.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methods",level:"1"},{id:"sec_3",title:"3. Measures",level:"1"},{id:"sec_4",title:"4. Statistical analyses",level:"1"},{id:"sec_5",title:"5. Results",level:"1"},{id:"sec_6",title:"6. Discussion",level:"1"},{id:"sec_7",title:"7. Conclusion",level:"1"},{id:"sec_8",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'TaichmanR. S.LobergR. D.MehraR.PientaK. J. The evolving biology and treatment of prostate cancer. J Clin Invest 117\n\t\t\t\t\t235161\n\t\t\t\t\t2007'},{id:"B2",body:'EtzioniR.PensonD.LeglerJ. M.et al. Overdiagnosis due to prostate-specific antigen screening: lessons from US prostate cancer incidence trends. J Natl Cancer Inst 94\n\t\t\t\t\t98190\n\t\t\t\t\t2002'},{id:"B3",body:'MillerD. C.GruberS. B.HollenbeckB. K.MontieJ. E.WeiJ. T. Incidence of initial local therapy among men with lower-risk prostate cancer in the United States. J Natl Cancer Inst 98\n\t\t\t\t\t113441\n\t\t\t\t\t2006'},{id:"B4",body:'HarlanS. 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October\n\t\t\t\t\t2008 Available from: http://www.auanet.org/content/about-us/members/membersprofile. pdf. Accessed 28 January 2009.'},{id:"B43",body:'MohanR.BeydounH.Barnes-ElyM. L.LeeL.DavisJ. W.LanceR.SchellhammerP. Patients’ survival expectations before localized prostate cancer treatment by treatment status. J Am Board Fam Med. 2009 May-Jun;22\n\t\t\t\t\t3\n\t\t\t\t\t24756 .'},{id:"B44",body:'BeydounH. A.MohanR.BeydounM. A.DavisJ.LanceR.SchellhammerP. Development of a scale to assess patient misperceptions about treatment choices for localized prostate cancer. British Journal of Urology International, 2010Fe Feb 11. [Epub ahead of print]. Available at: http://www3.interscience.wiley.com/cgi-bin/fulltext/123280002/PDFSTART'},{id:"B45",body:'MohanR.BeydounH.DavisJ.LanceR.SchellhammerP. Feasibility of using guidelines to choose treatment for prostate cancer. Can J Urol. 17\n\t\t\t\t\t497584\n\t\t\t\t\t2010 Abstract available at: http://www.ncbi.nlm.nih.gov/pubmed/20156376'},{id:"B46",body:'MohanR.SchellhammerP. Treatment options in Localized Prostate Cancer. American Family Physician, August 15. 2011. 84\n\t\t\t\t\t4\n\t\t\t\t\t41320 .'},{id:"B47",body:'ChouR.CroswellJ. M.DanaT.BougatsosC.BlazinaI.FuR.GleitsmannK.KoenigH. C.LamC.MaltzA.RuggeJ. B.LinK. Screening for Prostate Cancer: A Review of the Evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2011Oc Oct 7. [Epub ahead of print]'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ravinder Mohan",address:null,affiliation:'
Department of Family and Community Medicine,Eastern Virginia Medical School, Norfolk, Virginia, USA
Department of Family and Community Medicine,Eastern Virginia Medical School, Norfolk, Virginia, USA
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\n
1. Introduction
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Atmospheric deposition of nitrogen and sulfur is a growing and significant problem for the environment in many parts of the world. However, in urban areas it has become a concern due to the increase in atmospheric emission of gases and particulate that entails consequences for the environment and for the health of population. Sulfur dioxide (SO2), nitrogen oxide (NOx), and ammonia (NH3) are usually produced by anthropogenic activities. Industrial activities, vehicular emissions, and the burning of biomass are just some of the main sources of these pollutants in the atmosphere. The deposition of S and N occurs as a result of removal processes, either from precipitation (wet deposition) or from the deposit of particulate material or gas adsorption (dry deposition), and is associated with the acidification of soils and surface waters. Deposition of sulfur compounds results in the modification of the chemistry and biology of soil and water bodies such as the decrease in pH. On the other hand, the deposition of nitrogen compounds causes changes through the direct acidification of soils and natural water, or the saturation of nitrogen in vegetation species, which leads to the loss of vitality of diverse ecosystems. In addition, the deposition of N and S can cause deterioration to historical monuments and diverse materials [1]. Despite its importance, in Mexico, the monitoring of the deposition of these compounds, as well as the evaluation of their spatial and temporal distribution, and the estimation of their effects on ecosystems have not been sufficiently studied. Although in Mexican territory there are many cities with significant urban and industrial development, many of which are close to valuable historical heritage or important ecological zones, with the exception of the surrounding areas to the metropolitan zone of the Valley of Mexico, there are few air pollution studies available [2] One of the main reasons that limits the study of wet atmospheric deposition is that their study requires expensive automatic samplers that require compliance with certain specifications for installation and operation; while, in the case of dry deposition, standardized techniques are not available. In this regard, some authors [3] have proposed the use of passive samplers based on ion exchange resins for the monitoring of atmospheric deposition, this type of device allows to study several points simultaneously due to its low cost and simple design.
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On the other hand, the state of Nuevo Leon has been characterized by its accelerated urban and industrial growth, which places it within the three main metropolitan areas of the country and the second with the greatest territorial extension. Additionally, the city of Monterrey is the second city in the country with the highest reports of air pollution and subsequent effects not only on public health but also on ecosystems. Previous studies in this region have shown significant correlations between the wind direction and temperature inversions and contaminant transport from regional sources. That is, the pollutants in the MAM have a seasonal component as a result of the influence of these transport processes, resulting in a greater concentration and deposition of pollutants at certain climatic periods of the year. Likewise, in addition to the contribution by regional transport, there are also significant emissions from local industrial sources and vehicular sources that may result in background levels above the reference values considered as acceptable. However, since in the case of atmospheric deposition, it is not a criterion contaminant, that is, there is not a standard or reference value that regulates it, it is necessary to carry out monitoring studies at a medium or long term to establish a baseline that allows to perform an environmental diagnosis of the area and infer its possible effects. Notwithstanding, in the MAM, some authors [4, 5, 6, 7] have measured wet and dry atmospheric deposition; these studies have been carried out at a short term and systematic measurements that allow a proper diagnosis considering the seasonal and spatial components are not available. Therefore, the present work focuses on the mapping and study of the seasonal and spatial variability of N and S atmospheric deposition in the metropolitan area of Monterrey (MAM), Nuevo Leon, during three climatic seasons (dry, rainy, and cold fronts) using passive samplers based on ionic exchange resin at ten points distributed throughout nine municipalities of MAM.
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2. Study area
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The metropolitan area of Monterrey (MAM) is located to the northeast of the country in Nuevo Leon (25°42′26.53 N, 100°17′29.36 W). In 2015, it registered a total of 4,437,643 inhabitants within a surface of 6357 km2, being the third most populated city in Mexico only after Guadalajara and Mexico City; and the second in territorial extension. Worldwide, MAM occupies the 17th place, while in Latin America, it ranks number 10. Also, it was considered by Forbes in 2010 as the fourth most intelligent city in the world, with a great capacity of sustainable growth. MAM is located 913 km from Mexico City. It is known as “The City of the Mountains” due to the orographic formations existing within and in the surroundings of the city and, because of this, MAM exhibits serious air pollution problems. MAM climate is considered extreme, and according to Köppen climatic classification, it has warm and semi-arid climate (BSh), with an annual precipitation from 431.1 to 1300 mm. To assess the spatial and temporal distribution of N and S deposition fluxes, ten sampling sites were selected along MAM. The location of these sampling sites corresponds to the location of automatic monitoring stations of SIMA (Integral System of Environmental Monitoring of Monterrey). The specific location of these sampling sites and the name of each automatic monitoring station are presented in Figure 1.
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Figure 1.
Sampling sites location along MAM.
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3. Methodology
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3.1. Sampling
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The characterization of complex spatial patterns as atmospheric deposition of N and S in a given area requires simple monitoring equipment, which is cheap, easy to operate, and does not require frequent visits to the field. Throughfall deposition consists of solute collected in atmospheric deposition. This method is widely used to estimate the inputs of atmospheric deposit to the forests ecosystems, since, they include both, dry and wet deposition; therefore, this kind of passive sampler constitutes a good choice to obtain a reliable estimation of atmospheric inputs of N and S in a given ecosystem [8]. Passive samplers type throughfall are based in collectors of ionic exchange resin (IER). They consist of a funnel connected to a column that contains a mixed bed of ionic exchange resin (Amberlite™ IRN150). Deposition falls on the surface of the funnel, washing toward the inside of the column. The main advantage of this type of device is that it can be used during long periods of time (e.g., months) and the equipment has a very low cost, allowing to increase the number of sampling points in a given area. Therefore, with this kind of collector, it is possible to display a great number of them to characterize spatial patterns in deposition with a high resolution [9]. Nitrate, sulfate, and ammonium (NO3−, SO42− y NH4+.) can be exchanged in IER for cations and anions, respectively, and then be trapped by functional groups with opposite electric charges. In this study, a design of a mixed resin bed was chosen, since this kind of resin captures both, anions and cations.
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Throughfall deposition was collected in MAM, Nuevo Leon, Mexico, from February 26, 2017, to February 26, 2018, in ten sampling sites (Figure 1) which correspond to automatic monitoring stations of SIMA, by using deposition collectors based on IER operated and built according to [3, 10]. IER devices consist of funnels covered with a mesh (to prevent the fall of solid material such as leaves and insects) that are attached to PVC tubes. Inside these tubes, 30 g of IER are placed (where ions of interest are retained). Each tube is sealed with glass fiber at the bottom (as a platform or support for the resin) and at the top (as a filter). The resin tube is placed inside an outer PVC tube (shadow tube), which protects resin from solar radiation and helps avoid changes in its physical and chemical properties due to solar radiation. The lower end of the inner tube (resin tube) is closed or open by using a PVC valve to allow the hydrological flux to drain or not. Finally, resin tubes were placed in open areas at each sampling site within SIMA facilities. This exposition period allowed to obtain a data set for three seasons of 4 months each, corresponding to dry, rainy, and cold fronts or Norths seasons on an annual basis.
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3.2. Chemical analysis
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3.2.1. IER extraction procedure
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To carry out this process, an extraction system specially designed for this purpose was built. This system consisted of a PVC tube 5 mm (ID) and 15 cm in length, adapted to each collector with the resin to be extracted. Glass fiber is removed with tweezers to verify that the drain hole was not dirty. After this, each resin tube is labeled and it is verified that the PVC valve is closed. All columns are placed in vertical position, and then, the resin tubes are washed with 100 ml of deionized water, allowing a repose of 20 minutes. Simultaneously, the threaded connections are revised to identify leaks. In the case of one leak identification, the joints are tightened, and if necessary, Teflon™ tape is added. Once, 20 minutes have elapsed, the valve opens so that a drip rate of 2 drops by second is obtained. A continuous drip is maintained during 10 minutes until drainage is completed. This rinse is discarded. Then, it is ensured that the PVC valve is closed, and 100 ml of 2 N KCl extraction solution is added, and allowed to repose for 20 minutes. Again, the PVC valve is open so that a drip rate of 2 drops by second is obtained. This continuous drip is maintained for 10 minutes. Finally, the valve is open to allow the remainder solution to leave the resin tube until the drainage is completed. Once, the extracts of the samples are obtained, they are stored and refrigerated at 4°C until analysis.
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3.2.2. Ammonium determination
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NH4+ was determined by using blue indophenol method, whose color intensity is proportional to the ammonium concentration in the sample. Determination was done by colorimetry at a wave length of 630 nm. Color formation is completed after 10 minutes and remains stable for 24 hours. Once absorbances of the samples are obtained, a quantification process was done to obtain ammonium concentrations by using a calibration curve [11].
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3.2.3. Sulfate determination
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Sulfate ion precipitates with barium chloride in an acid medium (HCl) forming crystals of barium sulfate. The spectral absorption of the barium sulfate suspension is measured at 420 nm by using a UV-Visible spectrophotometer. Sulfate concentration is determined comparing the absorbance lectures with a calibration curve, by using the turbidimetric method [12].
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3.2.4. Nitrate determination
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Nitrate anion present in the sample reacts with alkaloid brucine in an acid medium (H2SO4), oxidizing it and producing cocoteline, with an unstable red color, which changes quickly to yellow, being determined colorimetrically at 410 nm [13].
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3.3. Meteorology analysis, criteria air pollutants and mapping
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3.3.1. Meteorology
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Speed and wind direction are determining factors in the composition of atmospheric deposition, since depending on the prevailing wind direction, it will have the influence of local continental or regional sources located upwind or the influence of maritime sources. In this study, the analysis of meteorological parameters at surface level was done by using data obtained from SIMA during the study period to identify possible anthropogenic or natural sources influencing the N and S levels found in the sampling sites. Wind roses were built to identify the prevailing wind direction in the study area. To assess the transport mechanism controlling deposition process in the study area by season, back air mass trajectories were estimated by the Lagrangian hybrid model HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) from US NOAA (National Atmospheric and Oceanic Administration).
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3.3.2. Criteria air pollutants concentration
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Database for the entire study period for each sampling point was obtained from SIMA of Monterrey for: CO, NO, NOx, NO2, SO2, O3, PM10 y PM2.5. From the obtained data, concentration roses were estimated for each air pollutant and for each sampling point by climatic season to identify if daily concentrations exceeded reference values someday. These concentration roses were useful to visualize in which wind direction there were higher concentrations and, then, to identify the possible sources contributing to these levels.
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3.3.3. Statistical analysis
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A Friedman test was used to determine if atmospheric deposition fluxes were different among sampling sites, according to land use or between climatic seasons. Friedman test is a non-parametric test that can be used with block design, in which the underlying assumptions are not as restrictive as those of an ANOVA procedure (XLStat v.2017). On the other hand, principal components analysis is a technique used to reduce the dimensionality of a data set. The projection according to which data is better represented is least squares. It converts a data set of variables possibly correlated in a data set of variables without lineal correlation called principal components. Descriptive, multivariate, and principal components analysis were carried out by using XLstat-Pro v. 2017.
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3.3.4. Deposition fluxes mapping
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One of the main uses of geo-statistical mapping consists in predicting new values from variables from the sample in a given area, which is referred as spatial prediction or spatial interpolation. Spatial distribution of a variable can be modeled either using a continuous model or a discrete or mixed model. On the other hand, temporal variability makes geo-statistical mapping expensive and complex. Taking into account that the seasonal periodicity in this work is regular for the studied environmental parameters, in this case, spatial variability was analyzed for each climatic period: Dry, rainy, and cold fronts or Norths seasons. The coordinates of each sampling site and the values for N and S deposition fluxes were the inputs used to derive the specific points in the maps showing the dispersion and the measured concentration for the different studied chemical compounds. In a second step, the concentrations at neighboring sampling points within the grid were averaged to attribute a value to the point. These points were the input for the interpolation procedure [14]. The deposition contours were smoothed by using the kriging method [15]. Kriging weights were estimated from a variogram, which measures the correlation grade among sampling values in the area as a function of the distance and direction. Digital images for MAM were obtained from INEGI, and these maps were integrated to build a base map in which concentration isolines obtained from Surfer program v. 10.0 were graphed, obtaining deposition fluxes maps in each studied zone by pollutant and by climatic season.
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4. Results and discussion
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4.1. Sulfate deposition fluxes
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4.1.1. By season
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The mean S deposition flux (as sulfate) during the dry season was 27.30 ± 10.34 Kg ha−1 yr.−1, with a maximum value of 47.69 Kg ha−1 yr.−1 in the site labeled as VI (Obispado) at the center of MAM. The average value obtained for the rainy season was 23.65 ± 4.14 Kg ha−1 yr.−1, with a maximum of 28.63 Kg ha−1 yr.−1 in site I (Escobedo) located to the north of MAM. On the other hand, the mean value for S deposition flux during the Norths season was 24.15 ± 7.39 Kg ha−1 yr.−1, with a maximum value of 31.48 Kg ha−1 yr.−1 in the sampling site labeled as V (Apodaca), located at the northeast side of MAM. From Figure 2a, it was observed that S deposition fluxes showed an evident seasonality, with the highest values during the dry season, and with the lowest values along the rainy season. However, from Friedman test, since p value is major than significance levels (α = 0.05), null hypothesis (H0) cannot be rejected; therefore, it can be concluded that there were no significant differences among S deposition fluxes by climatic season and that sulfate deposition levels have an evident influence from regional transport during all year.
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Figure 2.
Sulfate deposition fluxes by: (a) climatic season, (b)sampling site, and (c) land use for MAM during the study period; Nitrate deposition fluxes by: (d) climatic season, (e) sampling site, and (f) land use for MAM during the study period; Ammonium deposition fluxes by: (g) climatic season, (h) sampling site, and (i) land use for MAM during the study period.
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4.1.2. By sampling site
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In the analysis by sampling site, a mean value for S deposition flux of 25.03 ± 7.63 Kg ha−1 yr.−1 was obtained. According to Figure 2b, it can be observed that S deposition fluxes were higher in the sampling sites labeled as VI and V, which correspond to Obispado and Apodaca at the center and northeast of MAM. By applying Friedman test, p value is major than significance level (α = 0.05), and null hypothesis cannot be rejected. Therefore, it can be concluded that there were not significant differences in S deposition fluxes among sampling sites, suggesting an evident regional influence on MAM.
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4.1.3. By land use
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Sampling sites were grouped depending on their land use as: Rural (sites II and VII), Urban (sites I, IV, VI, VIII, IX and X), and Industrial (sites III and V). From Figure 2c, it can be observed that S deposition fluxes were higher at sites with an industrial land use (sites III and V), which correspond to San Bernabé and Apodaca, located to the northwest and northeast of MAM. A Friedman test was applied, and since p value is major than significance level (α = 0.05), the null hypothesis cannot be rejected, and it can be concluded that S deposition fluxes did not show significant differences by land use. This fact supports those found in the previous sections, where the regional character of sulfate due to residence time of SO2 was completely evident.
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4.2. Nitrate deposition fluxes
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4.2.1. By season
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The mean nitrate deposition flux value during the dry season was 3.30 ± 1.43 Kg ha−1 yr.−1, with a maximum of 4.38 Kg ha−1 yr.−1 corresponding to the sampling site VIII (San Pedro) located to the southwest of MAM. The average value obtained for rainy season was 6.54 ± 0.58 Kg ha−1 yr.−1, with the highest value (7.39) at the sampling site VIII (San Pedro). On the other hand, during the cold fronts season, the mean value for nitrate deposition flux was 3.26 ± 0.21 Kg ha−1 yr.−1, with a maximum value of 3.52 Kg ha−1 yr.−1 at site X (Juárez) located to the southeast of MAM. From Figure 2d, it can be observed that nitrate deposition fluxes were higher along the rainy season. From Friedman test, it was found that p value is minor than significance level (α = 0.05); therefore, it can be concluded that there were significant differences in nitrate deposition fluxes between rainy season and the rest of the year (dry and cold fronts seasons). It suggests that reactions in aqueous phase can be important, at the same time.
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4.2.2. By sampling site
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From the analysis of results by sampling site, from Figure 2e, it was found that nitrate deposition fluxes were higher in the sites VIII and III: San Pedro to the southwest and San Bernabé to the northwest of MAM. By applying a Friedman test, it was found that p value was minor than significance level (α = 0.05); therefore, null hypothesis must be rejected and it is concluded that there were significant differences between sites. It means that the influence of local sources was important. It agrees with the residence time of NO2 in the atmosphere, since it has been reported that nitrate is a local pollutant.
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4.2.3. By land use
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Sampling sites were grouped according to their land use as: Rural (sites II and VII), Urban (sites I, IV, VI, VIII, IX and X), and Industrial (sites III and V). From Figure 2f, it can be observed that nitrate deposition fluxes were higher in sampling sites with an urban land use (sites VIII and IX: San Pedro and La Pastora, located to the southwest and southeast of MAM). However, considering extreme values, these were found in sites with an industrial land use (most of the sites: IV, VI, VII, IX, and X). From Figure 2f, a great variability was observed, suggesting that local urban sources were mixed and emissions presented different magnitudes. It agrees with the different kinds of sources (industrial and urban) coexisting in this great metropolitan area. In spite of this, from Friedman test, it was found that p value was major than the significance level (α = 0.05); thus, the null hypothesis cannot be rejected, and therefore, it can be concluded that there were not significant differences between sampling sites by land use.
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4.3. Ammonium deposition fluxes
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4.3.1. By season
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The mean ammonium deposition flux during the dry season was 6.90 ± 3.88 Kg ha−1 yr.−1, with a maximum value of 13.31 Kg ha−1 yr.−1 in the sampling site labeled as VII (Santa Catarina) located to the southwest of MAM. During the rainy season, the average of ammonium deposition flux was 2.21 ± 1.49 Kg ha−1 yr.−1, with a maximum of 4.08 Kg ha−1 yr.−1 in site X (Juárez) located to the southeast of MAM. Finally, during the cold fronts season, ammonium deposition fluxes presented a mean value of 7.14 ± 3.49 Kg ha−1 yr.−1, with a peak value of 14.04 Kg ha−1 yr.−1 in the sampling site III (San Bernabé) at the northwest side of MAM. From Figure 2g, it is observed that, ammonium deposition fluxes were higher during the dry and cold fronts seasons. Ammonium levels were significantly lower during the rainy season, suggesting a washing effect during this season. From Friedman test, it was found that p value was minor than the significance level (α = 0.05), and the null hypothesis can be rejected; therefore, it is possible to conclude that there were significant differences between dry and cold fronts seasons and the rainy season. It suggests that, during the rainy season, a dilution effect could influence the ammonium deposition fluxes, considering that during the rest of the year, rains are scarce in MAM.
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4.3.2. By sampling site
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From Figure 2h, analyzing ammonium deposition fluxes by sampling site, the highest value was found in the sites VII and IV (Santa Catarina and San Nicolás), located to the southwest and northeast of MAM. Applying a Friedman test, it was found that p value is major than significance level (α = 0.05), and therefore, the null hypothesis cannot be rejected. Then, it can be concluded that there were not significant differences among sampling sites.
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4.3.3. By land use
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Sampling sites were grouped according to their land use as: Rural (sites II and VII), Urban (sites I, IV, VI, VIII, IX, and X), and Industrial (sites III and V). From Figure 2i, it can be observed that ammonium deposition fluxes were higher in sampling sites with an industrial and urban land use. The emission of amines and NH3 has been reported from vehicles (with the presence of a catalytic convertor that has enough stored hydrogen), where NO is reduced to NH3, and deposited as NH4+. Therefore, vehicular emissions could have an important influence on ammonium deposition in MAM. According to the Friedman test, p value was major than the significance level, thus null hypothesis cannot be rejected, and therefore, it can be concluded that there were not significant differences among sampling sites considering their land use.
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4.4. Meteorology and criteria air pollutants
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4.4.1. Site I Escobedo
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O3 showed a strong seasonal variation (Figure 3a) with the highest values during the dry season, (0.074–0.095 ppm) exceeding the reference value for 8 hours (70 ppb) [16]. Both, O3 and PM10 showed highest values when wind direction came from the East. In the case of PM10, Figure 3b shows that PM10 levels (≥75 μg m−3) exceeded the reference value for 24 hours (75 μg m−3) [17]. In Figure 3c, it can be observed that PM10 levels during the cold fronts season exceeded the reference value but winds also showed a great variability. In the case of PM2.5 (Figure 3d), its levels (≥75 μg m−3) exceeded the reference value (45 μg m−3) [17] for 24 hours during the cold fronts season when winds showed a great variability. Finally, winds came from the East during dry and rainy seasons (Figure 3e), showing a great variability during the cold fronts season (Figure 3f) with maximum wind speeds >7.9 m s−1 along the year.
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Figure 3.
Criteria air pollutants and meteorological conditions for site I (Escobedo) during the study period: (a) O3 dry season, (b) PM10 dry, and rainy seasons, (c) PM10 cold fronts season, (d) PM2.5 cold fronts season, (e) wind dry and rainy season, (f) wind cold fronts season.
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4.4.2. Site II Garcia
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From Figure 4a and b, it can be observed that O3 levels (0.074–0.095 ppm) were higher during the dry and rainy seasons, exceeding the reference value for 8 hours (70 ppb) [16] when wind came from the East. PM10 levels (≥75 μg m−3) were high during all year, exceeding reference value for 24 hours (75 μg m−3) [17], mainly when winds came from Northeast (Figure 4c and d). Finally, winds came from the East-Northeast during dry and rainy seasons (Figure 4e), showing a great variability during the cold fronts season (Figure 4f) with maximum wind speeds >7.9 m s−1 along the year. Both O3 and PM10 showed highest levels when winds had an East-Northeast component.
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Figure 4.
Criteria air pollutants and meteorological conditions for site II (García) during the study period: (a) O3 dry season, (b) O3 rainy season, (c) PM10 dry and rainy seasons, (d) PM10 cold fronts season, (e) wind dry and rainy season, (f) wind cold fronts season.
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4.4.3. Site III San Bernabe
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O3 levels (0.074–0.095 ppm) were higher during all year, exceeding the reference value for 8 hours (70 ppb) [16] when winds came from East-Southeast (Figure 5a and b). In addition, PM10 levels (≥75 μg m−3) also exceeded the reference value for 24 hours (75 μg m−3) [17] during all year, but showing highest values and a great variability in wind direction during the cold fronts season (Figure 5c and d). Finally, winds came from the East-Southeast during dry and rainy seasons (Figure 5e), showing a great variability during the cold fronts season (Figure 5f) with maximum wind speeds >7.9 m s−1 along the year. Both O3 and PM10 showed highest levels when winds had an East-Southeast component.
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Figure 5.
Criteria air pollutants and meteorological conditions for site III (San Bernabé) during the study period: (a) O3 dry and rainy season, (b) O3 cold fronts season, (c) PM10 dry and rainy seasons, (d) PM10 cold fronts season, (e) wind dry and rainy seasons, (f) wind cold fronts season.
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4.4.4. Site IV San Nicolas
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O3 levels (0.074–0.095 ppm) were high during all year, exceeding the reference value for 8 hours (70 ppb) [16] when winds came from North and East (Figure 6a–c). In addition, PM10 levels (≥75 μg m−3) also exceeded the reference value for 24 hours (75 μg m−3) [17] during all year when winds came from East and North, but showing highest values and a great variability in wind direction during the cold fronts season (Figure 6d–f). Finally, winds came from the North during dry season and from East during the rainy season (Figure 6g and h), showing a great variability during the cold fronts season (Figure 6i) with maximum wind speeds >7.9 m s−1 along the year. Both O3 and PM10 showed highest levels when winds had a North and East component.
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Figure 6.
Criteria air pollutants and meteorological conditions for site IV (San Nicolás) during the study period: (a) O3 dry season, (b) O3 rainy season, (c) O3 cold fronts season, (d) PM10 dry season, (e) PM10 rainy season, (f) PM10 cold fronts season, (g) wind dry season, (h) wind rainy season, (i) wind cold fronts season.
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4.4.5. Site V Apodaca
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From Figure 7a, it can be observed that O3 levels (≥0.095 ppm) were higher during the dry season when winds came from Northeast, exceeding the reference value for 8 hours (70 ppb) [16]. PM10 levels (≥75 μg m−3) were high during all year, exceeding reference value for 24 hours (75 μg m−3) [17]; mainly when winds came from North and Northwest (Figure 7b and c). PM2.5 levels (≥44 μg m−3) were high during rainy and cold fronts seasons when wind direction was from North, exceeding the reference value (45 μg m−3) [17] for 24 hours (Figure 7d). Winds came from the Northwest and North during dry and wet (rainy and cold fronts) seasons, respectively (Figure 7e and f), with maximum wind speeds >7.9 m s−1 along these seasons. O3, PM10, and PM2.5 showed highest levels when winds had a North-Northwest component.
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Figure 7.
Criteria air pollutants and meteorological conditions for site V (Apodaca) during the study period: (a) O3 dry season, (b) PM10 dry season, (c) PM10 rainy and cold fronts seasons, (d) PM2.5 rainy and cold fronts seasons, (e) wind dry season, (f) wind rainy and cold fronts seasons.
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4.4.6. Site VI Obispado
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From Figure 8a, it can be observed that O3 levels (≥0.095 ppm) were higher during the dry season when winds came from Northeast, exceeding the reference value for 8 hours (70 ppb) [16]. PM10 levels (≥75 μg m−3) were high during all year, exceeding reference value for 24 hours (75 μg m−3) [17], mainly when winds came from Northeast and Southwest (Figure 8b). PM2.5 levels were ≥44 μg m−3 during all year when wind direction was from Northeast, exceeding the reference value (45 μg m−3) [17] for 24 hours (Figure 8c and d). In addition, winds came from the Northeast during all year, showing a great variability during cold fronts season (Figure 8e and f), with maximum wind speeds >7.9 m s−1 along this season. O3, PM10, and PM2.5 showed highest levels when winds had a Northeast component.
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Figure 8.
Criteria air pollutants and meteorological conditions for site VI (Obispado) during the study period: (a) O3 dry, rainy and cold fronts seasons, (b) PM10 dry, rainy and cold fronts seasons, (c) PM2.5 dry and rainy seasons, (d) PM2.5 cold fronts season, (e) wind dry and rainy season, (f) wind cold fronts season.
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4.4.7. Site VII Santa Catarina
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O3 levels (≥0.095 ppm) were high during all year, being higher during the dry and rainy seasons, exceeding the reference value for 8 hours (70 ppb) [16] when winds came from North (Figure 9a and b). In addition, PM10 levels (≥75 μg m−3) also exceeded the reference value for 24 hours (75 μg m−3) [17] during all year and mainly when winds came from North (Figure 9c). PM2.5 levels were ≥44 μg m−3 during dry season when wind direction was from North, exceeding the reference value (45 μg m−3) [17] for 24 hours (Figure 9d). According to Figure 9e, O3 levels decreased significantly during the rainy season without showing exceedances to reference value. Finally, winds came from the North during all year (Figure 9f), with maximum wind speeds >7.9 m s−1 along the year. O3, PM10, and PM2.5 showed highest levels when winds had a North component.
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Figure 9.
Criteria air pollutants and meteorological conditions for Site VII (Santa Catarina) during the study period: (a) O3 dry and rainy seasons, (b) O3 cold fronts season, (c) PM10 dry, rainy and cold fronts seasons, (d) PM2.5 dry season, (e) PM2.5 rainy season, (f) wind all year.
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4.4.8. Site VIII San Pedro
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From Figure 10a and b, it can be observed that O3 levels (0.074–0.095 ppm) were higher during the dry season when winds came from East-Northeast, exceeding the reference value for 8 hours (70 ppb) [16]. PM10 levels (≥75 μg m−3) were high during all year, exceeding reference value for 24 hours (75 μg m−3) [17]; mainly when winds came from East-Northeast (Figure 10c and d). Finally, winds came from the Northeast during all year, showing a great variability during cold fronts season (Figure 10e and f), with maximum wind speeds >7.9 m s−1 along this season. Both O3 and PM10 showed highest levels when winds had a Northeast component.
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Figure 10.
Criteria air pollutants and meteorological conditions for Site VIII (San Pedro) during the study period: (a) O3 dry and rainy seasons, (b) O3 cold fronts season, (c) PM10 dry and rainy seasons, (d) PM10 cold fronts season, (e) wind dry and rainy seasons, (f) wind cold fronts season.
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4.4.9. Site IX La Pastora
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O3 levels (0.074–0.095 ppm) were high during dry and rainy seasons, exceeding the reference value for 8 hours (70 ppb) [16] when winds came from North-Northeast (Figure 11a). In addition, PM10 levels (≥75 μg m−3) were high during all year and also exceeded the reference value for 24 hours (75 μg m−3) [17], when winds came from North-Northeast (Figure 11b). PM2.5 levels were ≥44 μg m−3 during all year, being higher during cold fronts season, and when wind direction was from North-Northeast, exceeding the reference value (45 μg m−3) [17] for 24 hours (Figure 11c). Finally, winds came from the North-Northeast during all year (Figure 11d–f) with maximum wind speeds >7.9 m s−1 along the year, and showing a greater variability in wind direction during cold fronts season. O3, PM10, and PM2.5 showed highest levels when winds had a Northeast component.
\n
Figure 11.
Criteria air pollutants and meteorological conditions for site IX (La Pastora) during the study period: (a) O3 dry, rainy and cold fronts seasons, (b) PM10 dry, rainy, and cold fronts seasons, (c) PM2.5 dry, rainy, and cold fronts seasons, (d) wind dry season, (e) wind rainy season, (f) wind cold fronts season.
\n
\n
\n
4.4.10. Site X Juarez
\n
Figure 12a shows that CO levels (8.5–11 ppm) were higher during cold fronts season, reaching the upper limit value established in the air quality standard (11 ppm) for 8 hours [18]. From Figure 12b and c, it can be observed that O3 levels (≥0.095 ppm) were high during dry season when winds came from Southeast, exceeding in both cases, the reference value for 8 hours (70 ppb) [16]. PM10 levels (≥75 μg m−3) were high during all year, exceeding reference value for 24 hours (75 μg m−3) [17], when winds came from Southeast during dry and rainy season (Figure 12d) and from Northwest and Southeast during cold fronts season, showing a greater variability in wind direction (Figure 12e). Finally, winds came from the Southeast during dry and rainy seasons, and from Northwest during cold fronts season (Figure 12f), with maximum wind speeds >7.9 m s−1 along the year. O3 and PM10 showed highest levels when winds had a Southeast component most part of the year and a Northwest component during cold fronts season, suggesting a seasonal behavior for these pollutants. However, in the case of CO behavior, it was completely different, with the highest levels (even exceeding the air quality standard) during cold fronts season with winds coming from Southeast and Northwest.
\n
Figure 12.
Criteria air pollutants and meteorological conditions for site X (Juarez) during the study period: (a) CO cold fronts season, (b) O3 dry season, (c) O3 rainy and cold fronts seasons, (d) PM10 dry and rainy seasons, (e) PM10 cold fronts season, (f) wind all year.
\n
\n
\n
\n
4.5. Mapping N and S deposition fluxes and reference values
\n
In Mexico, reference values to compare the current deposition fluxes of N and S are not available. However, critical loads have been estimated for European ecosystems and some sites in the United States. A critical load value of 5 Kg N ha−1 yr.−1 has been proposed for alpine ecosystems [19], whereas for some sites in North America, values of 3–8 Kg N ha−1 yr.−1 for New Mexico and 4–7 Kg N ha−1 yr.−1 for California have been proposed [20]. In the case of S deposition, a critical value of 3 Kg S ha−1 yr.−1 has been reported for very sensitive areas in Europe, whereas for natural forests, a reference value of 2–5 Kg S ha−1 yr.−1 has been proposed [21]. In this study, mean N and S throughfall deposition fluxes were 4.88 and 25.03 Kg ha−1 yr.−1, respectively. N deposition fluxes did not exceed the reference value reported for alpine ecosystems; however, they are almost in the upper limit of this reference value and similar to those found in New Mexico and California. In addition, N deposition levels found in MAM (Figure 13) are almost twice those reported by Escoffie [22] in Carmen Island (2.15 Kg N ha−1 yr.−1), Campeche; by Sánchez [23] in Orizaba Valley, Veracruz (1.44 Kg N ha−1 yr.−1); and by López [24] in Mérida, Yucatán (2.7 Kg N ha−1 yr.−1) and are almost four times those reported by García [25] in Atasta-Xicalango, Campeche (1.15 Kg N ha−1 yr.−1). On the other hand, S deposition fluxes in MAM exceeded almost eight times the critical load proposed for sensitive areas, and five times the upper reference value for natural forests in Europe. S deposition fluxes found in MAM were almost six times higher than those reported by Escoffie [22] in Carmen Island, Campeche (4.7 Kg S ha−1 yr.−1); and by López [24] in Mérida, Yucatán (4.07 Kg S ha−1 yr.−1), and almost three times higher than those reported by García [25] in Atasta-Xicalango, Campeche (8.57 Kg S ha−1 yr.−1). In spite of S levels in MAM being half of those reported by Sánchez [23] in Orizaba Valley (55.16 Kg S ha−1 yr.−1), the current S deposition fluxes in MAM represent a risk potential of acidification and impact on ecosystems in this region.
\n
Figure 13.
Spatial and temporal patterns for throughfall deposition fluxes of SO42− for (a) dry season, (b) rainy season, (c) cold fronts season; of NO3− for (d) dry season, (e) rainy season, (f) cold fronts season; and of NH4+ for (g) dry season, (h) rainy season, (i) cold fronts season in MAM during the study period.
\n
\n
\n
\n
5. Conclusions
\n
This chapter presents an overview of atmospheric pollution and its spatial and temporal variability in MAM, and from results, we can conclude that:
\n
N deposition fluxes: Nitrate deposition showed a seasonal pattern with the highest levels during the rainy season (suggesting that atmospheric reactions in aqueous phase play an important role in the removal process). In the case of ammonium, its deposition also presented a seasonal variation, with higher levels during the dry and cold fronts season in Santa Catarina municipality. N deposition fluxes did not exceed the critical load values reported for Europe and USA; however, these levels were higher than those reported for the southeast region of Mexico.
\n
S deposition fluxes: Sulfate deposition did not show significant differences between seasons and sampling points, suggesting that levels found probably correspond to background levels in MAM. Sulfate levels were relatively high in Obispado, Santa Catarina, and Escobedo municipalities. S deposition fluxes exceeded the limit values proposed for sensitive areas and natural forests in Europe, and were higher than those reported at the southeast (SE) of the country, but lower than those found at the center of Mexico. It suggests that S deposition could be a potential risk for ecosystems and historical heritage in MAM.
\n
CO: Juárez municipality was the only sampling site that showed exceedances to the reference value established in the current regulation, this municipality is located to the east of MAM, and its levels were higher when wind came from N.
\n
O3: Ozone levels exceeded the reference value of the current regulation in all sampling sites during the dry season when wind had an east component (E-SE-NE).
\n
PM10: PM10 levels exceeded the threshold value of the current regulation in all sites and during all year, its levels being higher when wind came from East (E-SE-NE).
\n
PM2.5: Obispado and La Pastora municipalities (center of MAM) showed the highest levels during all year, whereas in Escobedo and Apodaca (at the northern side of MAM), its levels were higher during the cold fronts season.
\n
In spite of the time scale in which deposition fluxes (by season) and criteria pollutants (by day) were different, we could identify an evident association between CO and nitrate, since both analysis showed that their levels were higher in Juarez municipality during cold fronts season (CO levels exceeded the regulation’s reference values and exhibited a different pattern regarding to the remaining sampling sites in MAM). It suggests that both, CO and nitrate had their origin in vehicular sources in this urbane zone highly polluted. On the other hand, a similarity was observed between deposition patterns of S and PM10-PM2.5 levels in MAM, since sulfate did not present significant differences in its spatial and seasonal variability; it suggests that levels found in this study remained constant all year, and correspond to the background levels for MAM. The same finding was obtained for PM10 and PM2.5 levels, since their levels exceeded the reference value established in the current regulation in all sampling sites. Regarding wind direction, an evident association with criteria pollutants was found, PM10 and O3 showed their highest levels when wind had an east component (E-SE-NE), which corresponds to the prevailing wind direction during all year in MAM. In addition, PM2.5 levels were higher when wind came from north. It suggests that sources located at north (N) and east (E) from MAM contributed significantly to pollution in MAM. Finally, this study suggests that, since O3 and PM10 levels exceeded the allowable maximum limit during all year and in all sampling sites, the implications that this fact may have on the population health in MAM could be serious.
\n
\n\n',keywords:"N deposition, S deposition, criteria pollutants, Monterrey, Mexico",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64124.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64124.xml",downloadPdfUrl:"/chapter/pdf-download/64124",previewPdfUrl:"/chapter/pdf-preview/64124",totalDownloads:524,totalViews:77,totalCrossrefCites:1,dateSubmitted:"April 20th 2018",dateReviewed:"June 12th 2018",datePrePublished:"November 5th 2018",datePublished:"April 24th 2019",dateFinished:null,readingETA:"0",abstract:"The objective of this study was to assess the spatial and temporal variability of N and S atmospheric deposition and its relation with criteria air pollutants (CAPs) and meteorological conditions (MCs) in the metropolitan area of Monterrey (MAM). Atmospheric deposition was collected in 10 sampling sites during 3 climatic periods by using passive samplers based on ionic exchange resins (IERs); simultaneously, CAP and MC were monitored. Ions were extracted from IER to determine nitrate, ammonium, and sulfate levels, and deposition fluxes were estimated. On the other hand, from CAP and MC, wind and concentration roses were built to identify the exceedances of the current regulations, and relationships between CAP and meteorological conditions. It was found that only S deposition fluxes exceeded critical load values proposed in Europe, suggesting that S deposition could be a serious threat in MAM. It was found that CO in Juárez sampling site and O3 and PM10 in all sampling sites showed exceedances of the current regulatory limits, showing seasonal and spatial patterns similar to N and S deposition fluxes. Deposition fluxes were mapped to identify critical zones or periods in which these fluxes could be higher as a result of the prevailing meteorological conditions.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64124",risUrl:"/chapter/ris/64124",signatures:"Rosa María Cerón Bretón, Julia Griselda Cerón Bretón, Jonathan\nKahl, Evangelina Ramírez Lara, Atl Víctor Córdova Quiroz, Alberto\nAntonio Espinosa Guzmán, Manuel Muriel García, Gilma Gabriela\nArenas Hernández, José Angel Solís Canul and Abril Rodríguez\nGuzmán",book:{id:"6975",title:"Air Pollution",subtitle:"Monitoring, Quantification and Removal of Gases and Particles",fullTitle:"Air Pollution - Monitoring, Quantification and Removal of Gases and Particles",slug:"air-pollution-monitoring-quantification-and-removal-of-gases-and-particles",publishedDate:"April 24th 2019",bookSignature:"Jorge Del Real Olvera",coverURL:"https://cdn.intechopen.com/books/images_new/6975.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"166103",title:"Dr.",name:"Jorge",middleName:null,surname:"Del Real Olvera",slug:"jorge-del-real-olvera",fullName:"Jorge Del Real Olvera"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"128592",title:"Dr.",name:"Julia Griselda",middleName:null,surname:"Cerón Bretón",fullName:"Julia Griselda Cerón Bretón",slug:"julia-griselda-ceron-breton",email:"jceronbreton@gmail.com",position:null,institution:null},{id:"169990",title:"Dr.",name:"Atl Victor",middleName:null,surname:"Cordova Quiroz",fullName:"Atl Victor Cordova Quiroz",slug:"atl-victor-cordova-quiroz",email:"acordova@delfin.unacar.mx",position:null,institution:null},{id:"255604",title:"Dr.",name:"Rosa Maria",middleName:null,surname:"Ceron Breton",fullName:"Rosa Maria Ceron Breton",slug:"rosa-maria-ceron-breton",email:"rosabreton1970@gmail.com",position:null,institution:{name:"Autonomous University of Carmen",institutionURL:null,country:{name:"Mexico"}}},{id:"255606",title:"Dr.",name:"Jonathan",middleName:null,surname:"Kahl",fullName:"Jonathan Kahl",slug:"jonathan-kahl",email:"kahl@uwm.edu",position:null,institution:null},{id:"255607",title:"Dr.",name:"Evangelina",middleName:null,surname:"Ramirez Lara",fullName:"Evangelina Ramirez Lara",slug:"evangelina-ramirez-lara",email:"evangelina.ramirez.lr@gmail.com",position:null,institution:null},{id:"255608",title:"Dr.",name:"Manuel",middleName:null,surname:"Muriel García",fullName:"Manuel Muriel García",slug:"manuel-muriel-garcia",email:"mmuriel00@hotmail.com",position:null,institution:null},{id:"255609",title:"MSc.",name:"Gilma Gabriela",middleName:null,surname:"Arenas Hernández",fullName:"Gilma Gabriela Arenas Hernández",slug:"gilma-gabriela-arenas-hernandez",email:"burbuja_1390@hotmail.com",position:null,institution:null},{id:"255610",title:"BSc.",name:"José Angel",middleName:null,surname:"Solís Canul",fullName:"José Angel Solís Canul",slug:"jose-angel-solis-canul",email:"joseangelsc2014@hotmail.com",position:null,institution:null},{id:"257312",title:"MSc.",name:"Abril",middleName:null,surname:"Rodríguez Guzmán",fullName:"Abril Rodríguez Guzmán",slug:"abril-rodriguez-guzman",email:"abrilrg708@gmail.com",position:null,institution:null},{id:"261738",title:"Dr.",name:"Alberto",middleName:null,surname:"Espinosa Guzmán",fullName:"Alberto Espinosa Guzmán",slug:"alberto-espinosa-guzman",email:"aaespino@uacam.mx",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Study area",level:"1"},{id:"sec_3",title:"3. Methodology",level:"1"},{id:"sec_3_2",title:"3.1. Sampling",level:"2"},{id:"sec_4_2",title:"3.2. Chemical analysis",level:"2"},{id:"sec_4_3",title:"3.2.1. IER extraction procedure",level:"3"},{id:"sec_5_3",title:"3.2.2. Ammonium determination",level:"3"},{id:"sec_6_3",title:"3.2.3. Sulfate determination",level:"3"},{id:"sec_7_3",title:"3.2.4. Nitrate determination",level:"3"},{id:"sec_9_2",title:"3.3. Meteorology analysis, criteria air pollutants and mapping",level:"2"},{id:"sec_9_3",title:"3.3.1. Meteorology",level:"3"},{id:"sec_10_3",title:"3.3.2. Criteria air pollutants concentration",level:"3"},{id:"sec_11_3",title:"3.3.3. Statistical analysis",level:"3"},{id:"sec_12_3",title:"3.3.4. Deposition fluxes mapping",level:"3"},{id:"sec_15",title:"4. Results and discussion",level:"1"},{id:"sec_15_2",title:"4.1. Sulfate deposition fluxes",level:"2"},{id:"sec_15_3",title:"4.1.1. By season",level:"3"},{id:"sec_16_3",title:"4.1.2. By sampling site",level:"3"},{id:"sec_17_3",title:"4.1.3. By land use",level:"3"},{id:"sec_19_2",title:"4.2. Nitrate deposition fluxes",level:"2"},{id:"sec_19_3",title:"4.2.1. By season",level:"3"},{id:"sec_20_3",title:"4.2.2. By sampling site",level:"3"},{id:"sec_21_3",title:"4.2.3. By land use",level:"3"},{id:"sec_23_2",title:"4.3. Ammonium deposition fluxes",level:"2"},{id:"sec_23_3",title:"4.3.1. By season",level:"3"},{id:"sec_24_3",title:"4.3.2. By sampling site",level:"3"},{id:"sec_25_3",title:"4.3.3. By land use",level:"3"},{id:"sec_27_2",title:"4.4. Meteorology and criteria air pollutants",level:"2"},{id:"sec_27_3",title:"4.4.1. Site I Escobedo",level:"3"},{id:"sec_28_3",title:"4.4.2. Site II Garcia",level:"3"},{id:"sec_29_3",title:"4.4.3. Site III San Bernabe",level:"3"},{id:"sec_30_3",title:"4.4.4. Site IV San Nicolas",level:"3"},{id:"sec_31_3",title:"4.4.5. Site V Apodaca",level:"3"},{id:"sec_32_3",title:"4.4.6. Site VI Obispado",level:"3"},{id:"sec_33_3",title:"4.4.7. Site VII Santa Catarina",level:"3"},{id:"sec_34_3",title:"4.4.8. Site VIII San Pedro",level:"3"},{id:"sec_35_3",title:"4.4.9. Site IX La Pastora",level:"3"},{id:"sec_36_3",title:"4.4.10. Site X Juarez",level:"3"},{id:"sec_38_2",title:"4.5. Mapping N and S deposition fluxes and reference values",level:"2"},{id:"sec_40",title:"5. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Cerón RM, Cerón JG, Muriel M, Anguebes F, Ramírez M, Zavala J, et al. Spatial and temporal distribution of throughfall deposition of nitrogen and sulfur in the mangrove forests associated to Terminos Lagoon. In: Nejadkoorki F, editor. Current Air Quality Issues. 1st ed. Rijeka Croacia: IntechOpen; 2015. pp. 147-164. DOI: 10.5772/59726\n'},{id:"B2",body:'Fenn M, Bauer L, Quevedo A, Rodriguez C. Nitrogen and sulfur deposition and forest nutrient status in the Valley of Mexico. Water, Air, and Soil Pollution. 1999;113:155-174. DOI: 10.1023/A:1005033008277\n'},{id:"B3",body:'Fenn M, Poth M. Monitoring nitrogen deposition in throughfall using ion exchange resin columns: A field test in the San Bernardino Mountains. Journal of Environmental Quality. 2004;33:2007-2014. DOI: 10.2134/jeq2004.2007\n'},{id:"B4",body:'Ramírez E, Miranda R, Gracia Y, Balderas I, Bravo H, Sosa R, et al. Chemical composition of rainwater in northeastern Mexico. Revista Atmósfera. 2010;23:213-224\n'},{id:"B5",body:'Cerón R, Cerón J, Aguilar C, Ramírez E, Ortínez J, Montalvo C, et al. Wet deposition fluxes and related atmospheric chemistry at three sites in Mexico. Open Journal of Air Pollution. 2014a;3:1-9. DOI: 10.4236/ojap.2014.31001\n'},{id:"B6",body:'Cerón R, Cerón J, Ramírez E, Aguilar C, Montalvo C, López U, et al. Variations in criteria pollutants and deposition fluxes of trace elements in metropolitan area of Monterrey, Mexico. In: Proceedings of the 7th International Conference on Environmental and Geological Science and Engineering (EG’14); 3-5 June 2014; Salerno, Italy. Salerno: WSEAS; 2014. pp. 321-328\n'},{id:"B7",body:'Casanova M, Corroy C. Análisis de la Depositación Atmosférica Seca en el Municipio de San Nicolás de los Garza, Nuevo León [thesis]. Ciudad del Carmen Campeche: Universidad Autónoma del Carmen; 2017\n'},{id:"B8",body:'Butler T, Likens G. A direct comparison of throughfall plus stemflow to estimates of dry and total deposition for sulfur and nitrogen. Atmospheric Environment. 1995;29:1253-1265\n'},{id:"B9",body:'Clow D, Roop H, Nanus L, Fenn M, Sextone G. Spatial patterns of atmospheric deposition of nitrogen and sulfur using ion-exchange resin collectors in Rocky Mountain National Park, USA. Atmospheric Environment. 2015;101:149-157. DOI: 10.1016/j.atmosenv.2014.11.027\n'},{id:"B10",body:'Simkin S, Lewis D, Weathers K, Lovett G, Schwartz K. Determination of sulfate, nitrate and chloride in throughfall using ion-exchange resins. Water, Air, and Soil Pollution. 2004;153:343-354. DOI: 10.1023/B:WATE.0000019958.59277\n'},{id:"B11",body:'Fresenius W, Quentin K, Schneider W. Water Analysis; A Practical Guide to Physico-chemical, Chemical and Microbiological Water Examination and Quality Assurance. 1st ed. Berlin/Heidelberg: Springer-Verlag; 1988. pp. 195-476. DOI: 10.1007/978-3-642-72610-1\n'},{id:"B12",body:'NMX-AA-074-SCFI-2014. Medición del ión sulfato en aguas naturales, residuales y residuales tratadas–Método de prueba. Dirección General de Normas. México, D.F; 2015\n'},{id:"B13",body:'NMX-AA-079-SCFI-2001. Análisis de Aguas - Determinación de Nitratos en Aguas Naturales, Potables, Residuales y Residuales Tratadas - Método de Prueba. Dirección General de Normas. México, D.F; 2000\n'},{id:"B14",body:'UBA. Manual on Methodologies and Criteria for Mapping Critical Levels/Loads and Geographical Areas Where they are Exceeded. 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Inorganic nitrogen storage in alpine snow pack in the Central Alps (Switzerland). Atmospheric Environment. 2005;39:2249-2259. DOI: 10.1016/atmosenv.2004.12.037\n'},{id:"B20",body:'Fenn M, Geiser L. Temperate Sierra. In: Pardo L, Robin-Abbot M, Driscoll C, editors. Assessment of nitrogen deposition effects and empirical critical loads of nitrogen for ecoregions of the United States. 1st ed. Delaware,OH: United States Department of Agriculture. General Technical Report NRS-80; 2011. pp. 175-180\n'},{id:"B21",body:'Grennfelt P, Nilsson J. Critical loads for sulphur and nitrogen. In: Report from a Workshop of Nordic Council Ministers; 19-24 March 1988; Skokloster, Sweden; 1988. 418 p\n'},{id:"B22",body:'Escoffie R, Cerón R, Cerón J, Guevara E, Carballo C, Benítez J, et al. Throughfall deposition of N and S to mangrove ecosystems in the Southeast of Mexico. In: Proceedings of the 7th International Conference on Environmental and Geological Science and Engineering (EG’14); 3-5 June 2014; Salerno, Italy. Salerno: WSEAS; 2014. pp. 59-65\n'},{id:"B23",body:'Sánchez V. Evaluación del depósito atmosférico de nitrógeno y azufre en la región de Córdoba-Orizaba [thesis]. Ciudad del Carmen Campeche: Universidad Autónoma del Carmen; 2016\n'},{id:"B24",body:'López D. Estimación de los flujos de depositación atmosférica de nitrógeno y azufre en la zona metropolitana de la ciudad de Mérida durante 3 temporadas climatológicas [thesis]. Ciudad del Carmen Campeche: Universidad Autónoma del Carmen; 2018\n'},{id:"B25",body:'García A, Cerón R, Cerón J, Aguilar C, Montalvo C, Muriel M, et al. Mapping temporal and spatial variation of sulphur and nitrogen deposition to a complex ecosystem in Campeche, México. Transactions on Ecology and the Environment. 2016;203:113-123. DOI: 10.2495/EID160111\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Rosa María Cerón Bretón",address:"rosabreton1970@gmail.com",affiliation:'
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Dr. Biswas completed his Ph.D in Biotechnology at Banaras Hindu University, India, following which he did his post-doctoral study at the Istituto di Ricerche Farmacologiche \\"Mario Negri\\" in Milan, Italy. He is a principal investigator at the Singapore Immunology Network (SIgN), Agency for Science, Technology & Research (A*STAR), Singapore. Dr. Biswas’s major contribution in this field was the first molecular characterization of tumor associated macrophages. 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IntechOpen publishes different types of publications
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EDITED VOLUME
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IntechOpen Edited Volumes are integrated collections of chapters about particular topics that present new areas of research or novel syntheses of existing research and, as such, represent perspectives from various authors.
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Edited Volumes can be comprised of different types of chapters:
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RESEARCH CHAPTER – A research chapter reports the results of original research thus contributing to the body of knowledge in a particular area of study.
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REVIEW CHAPTER – A review chapter analyzes or examines research previously published by other scientists, rather than reporting new findings thus summarizing the current state of understanding on a topic.
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CASE STUDY – A case study involves an in-depth, and detailed examination of a particular topic.
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PERSPECTIVE CHAPTER – A perspective chapter offers a new point of view on existing problems, fundamental concepts, or common opinions on a specific topic. Perspective chapters can propose or support new hypotheses, or discuss the significance of newly achieved innovations. Perspective chapters can focus on current advances and future directions on a topic and include both original data and personal opinion.
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INTRODUCTORY CHAPTER – An introductory chapter states the purpose and goals of the book. The introductory chapter is written by the Academic Editor.
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MONOGRAPHS
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Monographs is a self-contained work on a particular subject, or an aspect of it, written by one or more authors. Monographs usually have between 130 and 500 pages.
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TYPES OF MONOGRAPHS:
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Single or multiple author manuscript
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COMPACTS
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Compacts provide a mid-length publishing format that bridges the gap between journal articles, book chapters, and monographs, and cover content across all scientific disciplines.
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Compacts are the preferred publishing option for brief research reports on new topics, in-depth case studies, dissertations, or essays exploring new ideas, issues, or broader topics on the research subject. Compacts usually have between 50 and 130 pages.
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CONFERENCE PROCEEDINGS
\\n\\n
Collection of papers presented at conferences, workshops, symposiums, or scientific courses, published in book format
IntechOpen Edited Volumes are integrated collections of chapters about particular topics that present new areas of research or novel syntheses of existing research and, as such, represent perspectives from various authors.
\n\n
Edited Volumes can be comprised of different types of chapters:
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RESEARCH CHAPTER – A research chapter reports the results of original research thus contributing to the body of knowledge in a particular area of study.
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REVIEW CHAPTER – A review chapter analyzes or examines research previously published by other scientists, rather than reporting new findings thus summarizing the current state of understanding on a topic.
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CASE STUDY – A case study involves an in-depth, and detailed examination of a particular topic.
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PERSPECTIVE CHAPTER – A perspective chapter offers a new point of view on existing problems, fundamental concepts, or common opinions on a specific topic. Perspective chapters can propose or support new hypotheses, or discuss the significance of newly achieved innovations. Perspective chapters can focus on current advances and future directions on a topic and include both original data and personal opinion.
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INTRODUCTORY CHAPTER – An introductory chapter states the purpose and goals of the book. The introductory chapter is written by the Academic Editor.
\n\n
MONOGRAPHS
\n\n
Monographs is a self-contained work on a particular subject, or an aspect of it, written by one or more authors. Monographs usually have between 130 and 500 pages.
\n\n
TYPES OF MONOGRAPHS:
\n\n
Single or multiple author manuscript
\n\n
COMPACTS
\n\n
Compacts provide a mid-length publishing format that bridges the gap between journal articles, book chapters, and monographs, and cover content across all scientific disciplines.
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Compacts are the preferred publishing option for brief research reports on new topics, in-depth case studies, dissertations, or essays exploring new ideas, issues, or broader topics on the research subject. Compacts usually have between 50 and 130 pages.
\n\n
CONFERENCE PROCEEDINGS
\n\n
Collection of papers presented at conferences, workshops, symposiums, or scientific courses, published in book format
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