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Modeling of Environmental Pollution (Air/Water/Soil) and Exposure Assessment

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 9663

Special Issue Editors

Prof. Dr. Bin Zou

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: air pollution exposure assessment; GIS&RS based environmental modeling
Dr. Xuying Ma

E-Mail Website
Guest Editor
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
Interests: GIS&RS based environmental modeling

Special Issue Information

Dear Colleagues,

To deal with the global environmental pollution crisis, it is of great importance to identify spatiotemporal variations in air/water/soil pollution in urban areas, and to assess the associated health risks of exposure to these types of pollution. Thanks to the development of numerical simulation, remote sensing inversion, and statistical modeling approaches, as well as the advent of novel measuring techniques, such as low-cost sensors and mobile monitoring, we are able to study environmental pollution at multi-scales and carry out exposure assessments in various macro- and microenvironments at the individual and population levels.

This Special Issue of the International Journal of Environmental Research and Public Health focuses on the current state of knowledge in the study of environmental pollution, exposure assessment, and relevant health risks through the use of various methods.

The main topics covered in this Special Issue include, but are not limited to, the following:

  • Air/water/soil pollution modeling at large scales using numerical simulation, remote sensing, and statistical approaches;
  • Air/water/soil pollution modeling at local scales, based on measurements from low-cost sensors and mobile monitoring;
  • Investigating spatiotemporal migrations and multi-media interactions of air/water/soil pollution, such as air–soil settlement and soil–water migration;
  • Intelligent monitoring techniques and big data platforms for air/water/soil pollution prevention;
  • Assessment of exposure to air/water/soil pollution in multiple macro- and microenvironments, based on environmental modeling;
  • Health risks associated with exposure to air/water/soil pollution and solutions to mitigate their effects.

New research papers and reviews are welcome to this issue.

Prof. Dr. Bin Zou
Dr. Xuying Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban air/water/soil pollution
  • environmental modeling
  • multi-scale perspective
  • exposure assessment
  • big data platform
  • health risks

Published Papers (5 papers)

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Research

17 pages, 1567 KiB  
Article
Preventing Agricultural Non-Point Source Pollution in China: The Effect of Environmental Regulation with Digitization
by Weikun Zhang, Peng Gao, Zhe Chen and Hailan Qiu
Int. J. Environ. Res. Public Health 2023, 20(5), 4396; https://doi.org/10.3390/ijerph20054396 - 01 Mar 2023
Viewed by 1490
Abstract
Environmental regulation (ER) is essential to preventing agricultural non-point source pollution (ANSP). Prior research has focused on the effect of ER on agricultural pollution (AP), but little is known about the impact of ER following digitization on preventing AP, particularly ANSP. Based on [...] Read more.
Environmental regulation (ER) is essential to preventing agricultural non-point source pollution (ANSP). Prior research has focused on the effect of ER on agricultural pollution (AP), but little is known about the impact of ER following digitization on preventing AP, particularly ANSP. Based on the spatial heterogeneity, the effect of ER was examined using a geographic detector tool with provincial panel data from 2010 to 2020 in rural China. The results show that ER is a driver in preventing ANSP, primarily because of the constraint on farmers’ behavior. Digitization positively affects the prevention of ANSP, as the new impetus for the infrastructure, technology, and capital is supported. The interaction between ER and digitalization forms a driving effect on the prevention of ANSP, indicating that digitalization constitutes the path dependence of farmers’ rule acquisition and perception and addresses the “free riding” dilemma of farmers’ participation, thereby enabling the incentive of ER to make agricultural production green and efficient. These findings indicate that the endogenous factor of digitization allowing ER is essential to preventing ANSP. Full article
(This article belongs to the Special Issue Modeling of Environmental Pollution (Air/Water/Soil) and Exposure Assessment)
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22 pages, 5558 KiB  
Article
Spatial Equity of PM2.5 Pollution Exposures in High-Density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China
by Zhuoran Shan, Hongfei Li, Haolan Pan, Man Yuan and Shen Xu
Int. J. Environ. Res. Public Health 2022, 19(19), 12671; https://doi.org/10.3390/ijerph191912671 - 03 Oct 2022
Cited by 2 | Viewed by 1902
Abstract
In-depth studies have been conducted on the risk of exposure to air pollution in urban residents, but most of them are static studies based on the population of residential units. Ignoring the real environmental dynamics during daily activity and mobility of individual residents [...] Read more.
In-depth studies have been conducted on the risk of exposure to air pollution in urban residents, but most of them are static studies based on the population of residential units. Ignoring the real environmental dynamics during daily activity and mobility of individual residents makes it difficult to accurately estimate the level of air pollution exposure among residents and determine populations at higher risk of exposure. This paper uses the example of the Wuhan metropolitan area, high-precision air pollution, and population spatio-temporal dynamic distribution data, and applies geographically weighted regression models, bivariate LISA analysis, and Gini coefficients. The risk of air pollution exposure in elderly, low-age, and working-age communities in Wuhan was measured and the health equity within vulnerable groups such as the elderly and children was studied. We found that ignoring the spatio-temporal behavioral activities of residents underestimated the actual exposure hazard of PM2.5 to residents. The risk of air pollution exposure was higher for the elderly than for other age groups. Within the aging group, a few elderly people had a higher risk of pollution exposure. The high exposure risk communities of the elderly were mainly located in the central and sub-center areas of the city, with a continuous distribution characteristic. No significant difference was found in the exposure risk of children compared to the other populations, but a few children were particularly exposed to pollution. Children’s high-exposure communities were mainly located in suburban areas, with a discrete distribution. Compared with the traditional static PM2.5 exposure assessment, the dynamic assessment method proposed in this paper considers the high mobility of the urban population and air pollution. Thus, it can accurately reveal the actual risk of air pollution and identify areas and populations at high risk of air pollution, which in turn provides a scientific basis for proposing planning policies to reduce urban PM2.5 and improve urban spatial equity. Full article
(This article belongs to the Special Issue Modeling of Environmental Pollution (Air/Water/Soil) and Exposure Assessment)
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20 pages, 5083 KiB  
Article
High-Coverage Reconstruction of XCO2 Using Multisource Satellite Remote Sensing Data in Beijing–Tianjin–Hebei Region
by Wei Wang, Junchen He, Huihui Feng and Zhili Jin
Int. J. Environ. Res. Public Health 2022, 19(17), 10853; https://doi.org/10.3390/ijerph191710853 - 31 Aug 2022
Cited by 7 | Viewed by 1717
Abstract
The extreme climate caused by global warming has had a great impact on the earth’s ecology. As the main greenhouse gas, atmospheric CO2 concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites [...] Read more.
The extreme climate caused by global warming has had a great impact on the earth’s ecology. As the main greenhouse gas, atmospheric CO2 concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO2 concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO2 and various factors affecting the spatial distribution of CO2, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO2 column concentration (XCO2) with full spatial coverage in the Beijing–Tianjin–Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R2) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R2 = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO2 concentrations from 2015 to 2019 in the Beijing–Tianjin–Hebei region was conducted using the established model. The study of the spatial distribution of XCO2 concentration in the Beijing–Tianjin–Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO2 map has the potential to monitor regional carbon emissions and evaluate emission reduction. Full article
(This article belongs to the Special Issue Modeling of Environmental Pollution (Air/Water/Soil) and Exposure Assessment)
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14 pages, 2214 KiB  
Article
Evaluation of the Street Canyon Level Air Pollution Distribution Pattern in a Typical City Block in Baoding, China
by Jingcheng Zhou, Junfeng Liu, Songlin Xiang, Yizhou Zhang, Yuqing Wang, Wendong Ge, Jianying Hu, Yi Wan, Xuejun Wang, Ying Liu, Jianmin Ma, Xilong Wang and Shu Tao
Int. J. Environ. Res. Public Health 2022, 19(16), 10432; https://doi.org/10.3390/ijerph191610432 - 22 Aug 2022
Cited by 2 | Viewed by 1638
Abstract
Urban traffic pollution, which is strongly influenced by the complex urban morphology, has posed a great threat to human health. In this study, we performed a high-resolution simulation of traffic pollution in a typical city block in Baoding, China, based on the Parallelized [...] Read more.
Urban traffic pollution, which is strongly influenced by the complex urban morphology, has posed a great threat to human health. In this study, we performed a high-resolution simulation of traffic pollution in a typical city block in Baoding, China, based on the Parallelized Large-eddy simulation Model (PALM), to examine the distribution patterns of traffic-related pollutants and explore their relationship with urban morphology. Based on the model results, we conducted a multi-linear regression (MLR) analysis and found that the distribution of air pollutants inside the city block was dominated by both traffic emissions and urban morphology, which explained about 70% of the total variance in spatial distribution of air pollutants. Excluding the contribution of emissions, over 50% of the total variance can still be explained by the urban morphology. Among these urban morphological factors, the key factors determining the spatial distribution of air pollution are “Distance from the road” (DR), “Building Coverage Ratio” (BCR) and “Aspect Ratio” (H/W) of the street canyon. Specifically, urban areas with lower Aspect Ratio, lower BCR and larger DR are less affected by traffic pollution. Compiling these individual factors, we developed a complex Urban Morphology Pollution Index (UMPI). Each unit increase in UMPI is associated with a one percent increase of nearby traffic pollution contribution. This index can help urban planners to semi-quantitatively evaluate building groups which tend to trap or ventilate traffic pollution and thus help to reduce human exposure to street canyon level pollution through either traffic emission control or urban morphology amelioration. Full article
(This article belongs to the Special Issue Modeling of Environmental Pollution (Air/Water/Soil) and Exposure Assessment)
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Graphical abstract

12 pages, 1584 KiB  
Article
Synergistic Effects between Ambient Air Pollution and Second-Hand Smoke on Inflammatory Skin Diseases in Chinese Adolescents
by Mengting Liao, Yi Xiao, Shenxin Li, Juan Su, Ji Li, Bin Zou, Xiang Chen and Minxue Shen
Int. J. Environ. Res. Public Health 2022, 19(16), 10011; https://doi.org/10.3390/ijerph191610011 - 13 Aug 2022
Cited by 3 | Viewed by 2296
Abstract
Atopic dermatitis (AD), chronic hand eczema (CHE), and urticaria are common inflammatory skin diseases among adolescents and associated with air quality. However, the synergistic effects of ambient air pollution and second-hand smoke (SHS) have been unclear. We conducted a cross-sectional study including 20,138 [...] Read more.
Atopic dermatitis (AD), chronic hand eczema (CHE), and urticaria are common inflammatory skin diseases among adolescents and associated with air quality. However, the synergistic effects of ambient air pollution and second-hand smoke (SHS) have been unclear. We conducted a cross-sectional study including 20,138 Chinese college students where dermatological examinations and a questionnaire survey were carried out. A generalized linear mixed model was applied for the association between individualized exposure of O3, CO, NO2, SO2, PM2.5, and PM10 and the prevalence of inflammatory skin diseases. Interactions between air pollutants and SHS were analyzed. As a result, CO, NO2, SO2, PM2.5, and PM10 were positively correlated with the prevalence of AD, CHE, and urticaria. Higher frequency of SHS exposure contributed to increased probabilities of AD (p = 0.042), CHE (p < 0.001), and urticaria (p = 0.002). Of note, CO (OR: 2.57 (1.16–5.69) in third quartile) and NO2 (OR: 2.38 (1.07–5.27) in third quartile) had positive interactions with SHS for AD, and PM2.5 synergized with SHS for CHE (OR: 2.25 (1.22–4.15) for second quartile). Subgroup analyses agreed with the synergistic results. In conclusion, SHS and ambient air pollution are both associated with inflammatory skin diseases, and they have a synergistic effect on the prevalence of AD and CHE. Full article
(This article belongs to the Special Issue Modeling of Environmental Pollution (Air/Water/Soil) and Exposure Assessment)
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