International Journal of Environmental Research and Public Health

Research

20 pages, 1745 KiB

Open AccessArticle

Spatial Effect of Digital Economy on Particulate Matter 2.5 in the Process of Smart Cities: Evidence from Prefecture-Level Cities in China

by Jingrong Tan and Lin Chen

Int. J. Environ. Res. Public Health 2022, 19(21), 14456; https://doi.org/10.3390/ijerph192114456 - 4 Nov 2022

Cited by 7 | Viewed by 1869

Abstract

During the COVID-19 pandemic, the digital economy has developed rapidly. The airborne nature of COVID-19 viruses has attracted worldwide attention. Therefore, it is of great significance to analyze the impact of the digital economy on particulate matter 2.5 (PM_2.5) emissions. The [...] Read more.

During the COVID-19 pandemic, the digital economy has developed rapidly. The airborne nature of COVID-19 viruses has attracted worldwide attention. Therefore, it is of great significance to analyze the impact of the digital economy on particulate matter 2.5 (PM_2.5) emissions. The research sample of this paper include 283 prefecture-level cities in China from 2011 to 2019 in China. Spatial Durbin model was adopted to explore the spatial spillover effect of digital economy on PM_2.5 emissions. In addition, considering the impact of smart city pilot (SCP) policy, a spatial difference-in-differences (SDID) model was used to analyze policy effects. The estimation results indicated that (1) the development of the digital economy significantly reduces PM_2.5 emissions. (2) The spatial spillover effect of the digital economy significantly reduces PM_2.5 emissions in neighboring cities. (3) Smart city construction increases PM_2.5 emissions in neighboring cities. (4) The reduction effect of the digital economy on PM_2.5 is more pronounced in the sample of eastern cities and urban agglomerations. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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18 pages, 14377 KiB

Open AccessArticle

Examining the Potential Scaling Law in Urban PM2.5 Pollution Risks along with the Nationwide Air Environmental Effort in China

by Lei Yao, Wentian Xu, Ying Xu and Shuo Sun

Int. J. Environ. Res. Public Health 2022, 19(8), 4460; https://doi.org/10.3390/ijerph19084460 - 7 Apr 2022

Cited by 3 | Viewed by 2276

Abstract

Urban scaling law provides a quantitative understanding of the fundamental nonlinear properties of how cities work. Addressing this, this study intended to examine the potential scaling law that may lie in urban air pollution. With ground-monitored PM2.5 data and statistical socioeconomic factors in [...] Read more.

Urban scaling law provides a quantitative understanding of the fundamental nonlinear properties of how cities work. Addressing this, this study intended to examine the potential scaling law that may lie in urban air pollution. With ground-monitored PM2.5 data and statistical socioeconomic factors in 265 Chinese cities (2015–2019), a targeted analysis, based on the scaling power-law model and scale-adjusted metropolitan indicator (SAMI) was conducted. The main findings of this study were summarized as follows: (1) A significant sublinear scaling relationship between PM2.5 and urban population size indicated that air quality degradation significantly lagged behind urban growth, affirming the remarkable effectiveness of national efforts on atmospheric environment improvement. (2) SAMI analysis expressed the relative conflict risk between PM2.5 pollution and urbanization and showed significant spatial cluster characteristics. Cities in central China showed higher potential risk than other regions, and there was a clear southward tendency for the city clusters with increasing SAMIs during the study period. (3) During the study period, urbanization was not the reason affecting the human-land conflict in terms of air pollution. This study is significant in that it marked the first innovative incorporation of the scaling law model into an urban environmental risk study. It also offered a new perspective from which to reframe the urban PM2.5 pollution risk, along with the nationwide air environmental effort in China, which will benefit future research on multi-types of urban environmental issues. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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18 pages, 4967 KiB

Open AccessArticle

Meteorological Influences on Spatiotemporal Variation of PM_2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing–Tianjin–Hebei Region, China

by Suxian Wang, Jiangbo Gao, Linghui Guo, Xiaojun Nie and Xiangming Xiao

Int. J. Environ. Res. Public Health 2022, 19(3), 1607; https://doi.org/10.3390/ijerph19031607 - 30 Jan 2022

Cited by 13 | Viewed by 4373

Abstract

Understanding the spatiotemporal characteristics of PM_2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei [...] Read more.

Understanding the spatiotemporal characteristics of PM_2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM_2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM_2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m⁻³) in 2019. PM_2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM_2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM_2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM_2.5 pollution. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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14 pages, 10382 KiB

Open AccessArticle

Spatial Association of Urban Form and Particulate Matter

by Yunmi Park, Jiyeon Shin and Ji Yi Lee

Int. J. Environ. Res. Public Health 2021, 18(18), 9428; https://doi.org/10.3390/ijerph18189428 - 7 Sep 2021

Cited by 5 | Viewed by 2431

Abstract

Increasingly detrimental effects of fine particulate matter (PM) have been observed in Northeast Asia owing to its rapid economic development. Previous studies have found that dust, combustion, and chemical reactions are the major sources of PM; nevertheless, the spatial configuration of land use [...] Read more.

Increasingly detrimental effects of fine particulate matter (PM) have been observed in Northeast Asia owing to its rapid economic development. Previous studies have found that dust, combustion, and chemical reactions are the major sources of PM; nevertheless, the spatial configuration of land use and land cover, which is of most interest to planners and landscape architects, also influences the PM levels. Here, we attempted to unveil the relationship between PM and different types of land use cover (i.e., developed, agricultural, woody, grass, and barren lands) in 122 municipalities of Korea. Landscape ecology metrics were applied to measure the spatial configuration of land use pattern and spatial lag models by taking into account the transboundary nature of air pollution, allowing us to conclude the following regarding PM levels: (1) the size of land cover type matters, but their spatial configuration also determines the variations in PM levels; (2) the contiguity and proximity of landcover patches are important; (3) the patterns of grasslands (e.g., simple, compact, and cluster (with large patches) patterns) and woodlands (e.g., complex, contiguous, and cluster (with large patches) patterns) considered desirable for minimizing PM are dissimilar in terms of contiguity. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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17 pages, 3765 KiB

Open AccessArticle

Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM_2.5 Estimation

by Arezoo Mokhtari, Behnam Tashayo and Kaveh Deilami

Int. J. Environ. Res. Public Health 2021, 18(13), 7115; https://doi.org/10.3390/ijerph18137115 - 2 Jul 2021

Cited by 3 | Viewed by 2027

Abstract

Land use regression (LUR) models are used for high-resolution air pollution assessment. These models use independent parameters based on an assumption that these parameters are accurate and invariable; however, they are observational parameters derived from measurements or modeling. Therefore, the parameters are commonly [...] Read more.

Land use regression (LUR) models are used for high-resolution air pollution assessment. These models use independent parameters based on an assumption that these parameters are accurate and invariable; however, they are observational parameters derived from measurements or modeling. Therefore, the parameters are commonly inaccurate, with nonstationary effects and variable characteristics. In this study, we propose a geographically weighted total least squares regression (GWTLSR) to model air pollution under various traffic, land use, and meteorological parameters. To improve performance, the proposed model considers the dependent and independent variables as observational parameters. The GWTLSR applies weighted total least squares in order to take into account the variable characteristics and inaccuracies of observational parameters. Moreover, the proposed model considers the nonstationary effects of parameters through geographically weighted regression (GWR). We examine the proposed model’s capabilities for predicting daily PM_2.5 concentration in Isfahan, Iran. Isfahan is a city with severe air pollution that suffers from insufficient data for modeling air pollution with conventional LUR techniques. The advantages of the model features, including consideration of the variable characteristics and inaccuracies of predictors, are precisely evaluated by comparing the GWTLSR model with ordinary least squares (OLS) and GWR models. The

R^{2}

values estimated by the GWTLSR model during the spring and autumn are 0.84 and 0.91, respectively. The corresponding average

R^{2}

values estimated by the OLS model during the spring and autumn are 0.74 and 0.69, respectively, and the

R^{2}

values estimated by the GWR model are 0.76 and 0.70, respectively. The results demonstrate that the proposed functional model efficiently described the physical nature of the relationships among air pollutants and independent variables. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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15 pages, 5892 KiB

Open AccessArticle

Comparison of Spatial Modelling Approaches on PM₁₀ and NO₂ Concentration Variations: A Case Study in Surabaya City, Indonesia

by Liadira Kusuma Widya, Chin-Yu Hsu, Hsiao-Yun Lee, Lalu Muhamad Jaelani, Shih-Chun Candice Lung, Huey-Jen Su and Chih-Da Wu

Int. J. Environ. Res. Public Health 2020, 17(23), 8883; https://doi.org/10.3390/ijerph17238883 - 29 Nov 2020

Cited by 12 | Viewed by 3350

Abstract

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) [...] Read more.

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM₁₀) and nitrogen dioxide (NO₂) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM₁₀ variations and 46%, 47%, and 48% of NO₂ variations, respectively. The GTWR model performed better (R² = 0.51 for PM₁₀ and 0.48 for NO₂) than the other two models (R² = 0.49–0.50 for PM₁₀ and 0.46–0.47 for NO₂), LUR and GWR. In the PM₁₀ model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO₂ variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM₁₀ and NO₂, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM₁₀ and NO₂ concentration variations within areas across Asia. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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21 pages, 5281 KiB

Open AccessArticle

Investigating a Potential Map of PM_2.5 Air Pollution and Risk for Tourist Attractions in Hsinchu County, Taiwan

by Yuan-Chien Lin, Hua-San Shih, Chun-Yeh Lai and Jen-Kuo Tai

Int. J. Environ. Res. Public Health 2020, 17(22), 8691; https://doi.org/10.3390/ijerph17228691 - 23 Nov 2020

Cited by 3 | Viewed by 3311

Abstract

In the past few years, human health risks caused by fine particulate matters (PM_2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan’s Government enforced in 2017, “suspended particulate matter” has officially [...] Read more.

In the past few years, human health risks caused by fine particulate matters (PM_2.5) and other air pollutants have gradually received attention. According to the Disaster Prevention and Protection Act of Taiwan’s Government enforced in 2017, “suspended particulate matter” has officially been acknowledged as a disaster-causing hazard. The long-term exposure to high concentrations of air pollutants negatively affects the health of citizens. Therefore, the precise determination of the spatial long-term distribution of hazardous high-level air pollutants can help protect the health and safety of residents. The analysis of spatial information of disaster potentials is an important measure for assessing the risks of possible hazards. However, the spatial disaster-potential characteristics of air pollution have not been comprehensively studied. In addition, the development of air pollution potential maps of various regions would provide valuable information. In this study, Hsinchu County was chosen as an example. In the spatial data analysis, historical PM_2.5 concentration data from the Taiwan Environmental Protection Administration (TWEPA) were used to analyze and estimate spatially the air pollution risk potential of PM_2.5 in Hsinchu based on a geographic information system (GIS)-based radial basis function (RBF) spatial interpolation method. The probability that PM_2.5 concentrations exceed a standard value was analyzed with the exceedance probability method; in addition, the air pollution risk levels of tourist attractions in Hsinchu County were determined. The results show that the air pollution risk levels of the different seasons are quite different. The most severe air pollution levels usually occur in spring and winter, whereas summer exhibits the best air quality. Xinfeng and Hukou Townships have the highest potential for air pollution episodes in Hsinchu County (approximately 18%). Hukou Old Street, which is one of the most important tourist attractions, has a relatively high air pollution risk. The analysis results of this study can be directly applied to other countries worldwide to provide references for tourists, tourism resource management, and air quality management; in addition, the results provide important information on the long-term health risks for local residents in the study area. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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14 pages, 4634 KiB

Open AccessArticle

Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration

by Chin-Yu Hsu, Yu-Ting Zeng, Yu-Cheng Chen, Mu-Jean Chen, Shih-Chun Candice Lung and Chih-Da Wu

Int. J. Environ. Res. Public Health 2020, 17(19), 6956; https://doi.org/10.3390/ijerph17196956 - 23 Sep 2020

Cited by 10 | Viewed by 3606

Abstract

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to [...] Read more.

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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18 pages, 4461 KiB

Open AccessArticle

Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling

by Seung-Hyeop Lee and Kyung-Hwan Kwak

Int. J. Environ. Res. Public Health 2020, 17(18), 6915; https://doi.org/10.3390/ijerph17186915 - 22 Sep 2020

Cited by 18 | Viewed by 4204

Abstract

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside [...] Read more.

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NO_x), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM_2.5) concentration below the apartment building height. Atmospheric NO_x dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NO_x concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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12 pages, 1748 KiB

Open AccessArticle

Spatial Effect of Industrial Energy Consumption Structure and Transportation on Haze Pollution in Beijing-Tianjin-Hebei Region

by Meicun Li and Chunmei Mao

Int. J. Environ. Res. Public Health 2020, 17(15), 5610; https://doi.org/10.3390/ijerph17155610 - 4 Aug 2020

Cited by 20 | Viewed by 3332

Abstract

Haze pollution has a serious impact on China’s economic development and people’s livelihood. We used data on PM_2.5 concentration, industrial energy consumption structure, economic development and transportation in Beijing-Tianjin-Hebei and surrounding cities from 2000 to 2017, and analyzed the spatial effect of [...] Read more.

Haze pollution has a serious impact on China’s economic development and people’s livelihood. We used data on PM_2.5 concentration, industrial energy consumption structure, economic development and transportation in Beijing-Tianjin-Hebei and surrounding cities from 2000 to 2017, and analyzed the spatial effect of industrial energy consumption structure and traffic factors on haze pollution by using spatial autoregressive model (SAR) and spatial error model (SEM). The results indicated that: (1) The global spatial correlation analysis showed that haze pollution had a significant positive spatial correlation, and the local spatial correlation analysis showed that the high-high clusters of PM_2.5 were located in the south and middle of the region; (2) The change of industrial energy consumption structure was highly correlated with haze pollution, namely, the increase of industrial energy consumption led to the deterioration of environmental quality; (3) The change of economic development was highly correlated with haze pollution. There was no clear EKC relationship between haze pollution and economic development in Beijing-Tianjin-Hebei region and surrounding cities. However, the relationship was similar to inverted U-shaped curve; (4) The change of traffic jam was highly correlated with haze pollution, namely, the increase of fuel consumption per unit road area led to the deterioration of environmental quality. Based on the above results, from the perspective of space, the long-term measures for haze control in Beijing-Tianjin-Hebei and surrounding cities can be explored from the aspects of energy conservation and emission reduction, industrial transfer, vehicle emission control, traffic restrictions and purchase restrictions. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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16 pages, 9324 KiB

Open AccessArticle

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa

by Hasheel Tularam, Lisa F. Ramsay, Sheena Muttoo, Rajen N. Naidoo, Bert Brunekreef, Kees Meliefste and Kees de Hoogh

Int. J. Environ. Res. Public Health 2020, 17(15), 5406; https://doi.org/10.3390/ijerph17155406 - 27 Jul 2020

Cited by 16 | Viewed by 3611

Abstract

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM₁₀ and PM [...] Read more.

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM₁₀ and PM_2.5), sulphur dioxide (SO₂), and nitrogen dioxide (NO₂) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m³ for NO₂, SO₂, PM₁₀, and PM_2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m³ for NO₂, SO₂, PM₁₀, and PM_2.5, respectively). Furthermore, higher levels of NO₂ and SO₂ were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO₂ models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO₂ models were less robust with lower R² annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R² for PM₁₀ models ranged from 52% to 80% while for PM_2.5 models this range was 61–76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO₂ concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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18 pages, 5112 KiB

Open AccessArticle

Source Apportionment of Coarse Particulate Matter (PM₁₀) in Yangon, Myanmar

by Piyaporn Sricharoenvech, Alexandra Lai, Tin Nwe Oo, Min M. Oo, James J. Schauer, Kyi Lwin Oo and Kay Khine Aye

Int. J. Environ. Res. Public Health 2020, 17(11), 4145; https://doi.org/10.3390/ijerph17114145 - 10 Jun 2020

Cited by 15 | Viewed by 3448

Abstract

The Republic of the Union of Myanmar is one of many developing countries facing concerns about particulate matter (PM). Previously, a preliminary study of PM_2.5 in 2018 suggested that the main source of PM in Yangon, the former capital, was vehicle emissions. [...] Read more.

The Republic of the Union of Myanmar is one of many developing countries facing concerns about particulate matter (PM). Previously, a preliminary study of PM_2.5 in 2018 suggested that the main source of PM in Yangon, the former capital, was vehicle emissions. However, this suggestion was not supported by any chemical composition data. In this study, to fill that gap, we quantitatively determined source contributions to coarse particulate matter (PM₁₀) in Yangon, Myanmar. PM₁₀ samples were collected in Yangon from May 2017 to April 2018 and chemically analyzed to determine composition. Chemical composition data for these samples were then used in the Chemical Mass Balance (CMB) model to identify the major sources of particulate matter in this area. The results indicate that PM₁₀ composition varies seasonally according to both meteorological factors (e.g., precipitation and temperature) and human activities (e.g., firewood and yard waste burning). The major sources of PM in Yangon annually were dust, secondary inorganic aerosols (SIA), and secondary organic aerosols (SOA), while contributions from biomass burning were more important during the winter months. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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14 pages, 3484 KiB

Open AccessArticle

Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing–Tianjin–Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

by Jianhui Qin, Suxian Wang, Linghui Guo and Jun Xu

Int. J. Environ. Res. Public Health 2020, 17(5), 1598; https://doi.org/10.3390/ijerph17051598 - 2 Mar 2020

Cited by 17 | Viewed by 3805

Abstract

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM_2.5, PM₁₀, SO₂, NO₂ [...] Read more.

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) from 2015 to 2016, this study analyzed the spatial and temporal characteristics of air pollution and influencing factors in Henan Province, a key region of the BTH air pollution transmission channel. The result showed that non-attainment days and NAQI were slightly improved at the provincial scale during the study period, whereas that in Hebi, Puyang, and Anyang became worse. PM_2.5 was the largest contributor to the air pollution in all cities based on the number of non-attainment days, but its mean frequency decreased by 21.62%, with the mean occurrence of O₃ doubled. The spatial distribution of NAQI presented a spatial agglomeration pattern, with high-high agglomeration area varying from Jiaozuo, Xinxiang, and Zhengzhou to Anyang and Hebi. In addition, the NAQI was negatively correlated with sunshine duration, temperature, relative humidity, wind speed, and positively to atmospheric pressure and relative humidity in all four clusters, whereas relationships between socioeconomic factors and NAQI differed among them. These findings highlight the need to establish and adjust regional joint prevention and control of air pollution as well as suggest that it is crucially important for implementing effective strategies for O₃ pollution control. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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13 pages, 3845 KiB

Open AccessArticle

Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM_2.5

by Hone-Jay Chu and Muhammad Zeeshan Ali

Int. J. Environ. Res. Public Health 2020, 17(4), 1419; https://doi.org/10.3390/ijerph17041419 - 22 Feb 2020

Cited by 1 | Viewed by 2903

Abstract

Poor air quality usually leads to PM_2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence [...] Read more.

Poor air quality usually leads to PM_2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration–duration–frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. As a basis for deducing the extreme CDF relationship of PM_2.5, the function assumes that the extreme concentration is related to the duration and frequency. In addition, the spatial pattern estimation of extreme PM_2.5 is identified. The regional CDF identifies the regional extreme concentration with a specified duration and return period. The spatial pattern of extreme air pollution over 8 h duration shows the hotspots of air quality in the central and southwestern areas. Central and southwestern Taiwan is at high risk of exposure to air pollution. Use of the regional CDF analysis is highly recommended for efficient design of air quality management and control. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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12 pages, 3380 KiB

Open AccessArticle

PM_2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan

by Widya Liadira Kusuma, Wu Chih-Da, Zeng Yu-Ting, Handayani Hepi Hapsari and Jaelani Lalu Muhamad

Int. J. Environ. Res. Public Health 2019, 16(24), 4924; https://doi.org/10.3390/ijerph16244924 - 5 Dec 2019

Cited by 25 | Viewed by 5821

Abstract

Air pollution has emerged as a significant health, environmental, economic, and social problem all over the world. In this study, geospatial technologies coupled with a LUR (Land Use Regression) approach were applied to assess the spatial-temporal distribution of fine particulate (PM_2.5). [...] Read more.

Air pollution has emerged as a significant health, environmental, economic, and social problem all over the world. In this study, geospatial technologies coupled with a LUR (Land Use Regression) approach were applied to assess the spatial-temporal distribution of fine particulate (PM_2.5). In-situ observations of air pollutants from ground monitoring stations from 2016–2018 were used as dependent variables, while the land-use/land cover, a NDVI (Normalized Difference Vegetation Index) from a MODIS sensors, and meteorology data allocations surrounding the monitoring stations from 0.25–5 km buffer ranges were collected as spatial predictors from GIS and remote sensing databases. A linear regression method was developed for the LUR model and 10-fold cross-validation was used to assess the model robustness. The R² model obtained was 56% for DKI Jakarta, Indonesia, and 83% for Taipei Metropolis, Taiwan. According to the results of the PM_2.5 model, the essential predictors for DKI Jakarta were influenced by temperature, NDVI, humidity, and residential area, while those for the Taipei Metropolis region were influenced by PM₁₀, NO₂, SO₂, UV, rainfall, spring, main road, railroad, airport, proximity to airports, mining areas, and NDVI. The validation of the results of the estimated PM_2.5 distribution use 10-cross validation with indicated R² values of 0.62 for DKI Jakarta and 0.84 for Taipei Metropolis. The results of cross-validation show the strength of the model. Full article

(This article belongs to the Special Issue Spatial Modeling of Air Pollutant Variability)

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