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Spatio-Temporal Analysis of Air Pollution

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 14426

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Special Issue Editors

Dr. Zhenbo Wang

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Guest Editor

Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences, Beijing, China
Interests: urbanization; PM2.5

Dr. Kexin Li

E-Mail Website
Guest Editor

Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: urban; environmental health

Special Issue Information

Dear Colleagues,

Air pollution is one of the top serious environmental issues on our planet. Poor air quality has directly threatened both human health and the natural environment. The latest Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) shows that air pollution is still one of the leading risk factors for death globally. Effective control of air pollution has become the top priority of governments at all levels in recent years. For this reason, numerous studies have been developed focusing on the emission and transport of relevant air pollutants, chemical processes and physical effects in the atmosphere, air quality monitoring and management, air pollution exposure assessment and health effects, as well as impacts of the changing atmospheric composition on ecosystems and climate change in order to mitigate air pollution. However, a number of existing studies have simplified the problem into local air pollution situations but fail to address air pollution as a spatio-temporal problem.

Spatial and temporal variability of air pollution are key parameters in accurate assessment of health risks associated with air-pollutant exposure. A well understanding of spatio-temporal characteristics of air pollution is also required in the development of integrated interventions to prevent and control air pollution. Although spatial and temporal variations of multiple air pollutants have been reported recently, special emphasis and systematical research should still be devoted to spatial-temporal analysis of air pollution.

The purpose of this special issue is to provides a home for high quality work including but not limited to big data assimilation, mining and analysis of air pollution, air pollution models for short-term forecast and long-term projection, air-pollutant exposure and risk assessments, etc., with a focus on its specific angle of view to answer questions using spatial and spatio-temporal approaches, in hope of advancing our understanding of air pollution and providing scientific reference and decision-making assistance for the decision makers, researchers and engineers, thus to promote the safe and efficient development of regional air pollution treatment and population health protection.

Dr. Zhenbo Wang
Dr. Kexin Li
Guest Editors

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Published Papers (7 papers)

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Research

15 pages, 2950 KiB

Open AccessEditor’s ChoiceArticle

Application of Functional Principal Component Analysis in the Spatiotemporal Land-Use Regression Modeling of PM_2.5

by Mahmood Taghavi, Ghader Ghanizadeh, Mohammad Ghasemi, Alessandro Fassò, Gerard Hoek, Kiavash Hushmandi and Mehdi Raei

Atmosphere 2023, 14(6), 926; https://doi.org/10.3390/atmos14060926 - 25 May 2023

Cited by 2 | Viewed by 938

Abstract

Functional data are generally curves indexed over a time domain, and land-use regression (LUR) is a promising spatial technique for generating high-resolution spatial estimation of retrospective long-term air pollutants. We developed a methodology for the novel functional land-use regression (FLUR) model, which provides high-resolution spatial and temporal estimations of retrospective pollutants. Long-term fine particulate matter (PM_2.5) in the megacity of Tehran, Iran, was used as the practical example. The hourly measured PM_2.5 concentrations were averaged for each hour and in each air monitoring station. Penalized smoothing was employed to construct the smooth PM_2.5 diurnal curve using averaged hourly data in each of the 30 stations. Functional principal component analysis (FPCA) was used to extract FPCA scores from pollutant curves, and LUR models were fitted on FPCA scores. The mean of all PM_2.5 diurnal curves had a maximum of 39.58 µg/m³ at 00:26 a.m. and a minimum of 29.27 µg/m³ at 3:57 p.m. The FPCA explained about 99.5% of variations in the observed diurnal curves across the city using just three components. The evaluation of spatially predicted long-term PM_2.5 diurnal curves every 15 min provided a series of 96 high-resolution exposure maps. The presented methodology and results could benefit future environmental epidemiological studies. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Air Pollution)

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15 pages, 4338 KiB

Open AccessArticle

Functional Kriging for Spatiotemporal Modeling of Nitrogen Dioxide in a Middle Eastern Megacity

by Elham Ahmadi Basiri, Seyed Mahmood Taghavi-Shahri, Behzad Mahaki and Heresh Amini

Atmosphere 2022, 13(7), 1095; https://doi.org/10.3390/atmos13071095 - 12 Jul 2022

Cited by 1 | Viewed by 1526

Abstract

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they [...] Read more.

{NO}_{2}

) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term

{NO}_{2}

diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the

{NO}_{2}

concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Air Pollution)

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15 pages, 4221 KiB

Open AccessArticle

Interference of Urban Morphological Parameters in the Spatiotemporal Distribution of PM₁₀ and NO₂, Taking Dalian as an Example

by Yuan Su, Xuezheng Wu, Qinfeng Zhao, Dian Zhou and Xiangzhao Meng

Atmosphere 2022, 13(6), 907; https://doi.org/10.3390/atmos13060907 - 2 Jun 2022

Cited by 4 | Viewed by 1514

Abstract

Recently, air quality has become a hot topic due to its profound impact on the quality of the human living environment. This paper selects the tourist city of Dalian as the research object. The concentration and spatial distribution of PM₁₀ and NO₂ in the main urban area were analyzed during the peak tourist seasons in summer and winter. Simulations were used to explore the spatial and temporal variation patterns of PM₁₀ and NO₂, combining building and road density at different scales to reveal the coupling relationship between individual pollutant components and urban parameters. The results show that the PM₁₀ concentration is high in the center and NO₂ is concentrated in the northern district of Dalian City. In an area with a radius of 100 m, the dilution ratio of building density and road density to the concentration of the PM₁₀ pollutants is at least 43%. Still, the concentration of NO₂ is only coupled with road density. This study reveals the spatial and temporal variation patterns of PM₁₀ and NO₂ in Dalian, and finds the coupling relationship between the two pollutants and building density and road density. This study provides a reference for preventing and controlling air pollution in urban planning. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Air Pollution)

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17 pages, 2138 KiB

Open AccessArticle

Modeling the Determinants of PM2.5 in China Considering the Localized Spatiotemporal Effects: A Multiscale Geographically Weighted Regression Method

by Han Yue, Lian Duan, Mingshen Lu, Hongsheng Huang, Xinyin Zhang and Huilin Liu

Atmosphere 2022, 13(4), 627; https://doi.org/10.3390/atmos13040627 - 14 Apr 2022

Cited by 10 | Viewed by 2324

Abstract

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Air Pollution)

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14 pages, 7416 KiB

Open AccessArticle

Carbon and Trace Element Compositions of Total Suspended Particles (TSP) and Nanoparticles (PM_0.1) in Ambient Air of Southern Thailand and Characterization of Their Sources

by Muanfun Inerb, Worradorn Phairuang, Phakphum Paluang, Mitsuhiko Hata, Masami Furuuchi and Prasit Wangpakapattanawong

Atmosphere 2022, 13(4), 626; https://doi.org/10.3390/atmos13040626 - 14 Apr 2022

Cited by 10 | Viewed by 2453

Abstract

The concentration of total suspended particles (TSP) and nanoparticles (PM_0.1) over Hat Yai city, Songkhla province, southern Thailand was measured in 2019. Organic carbon (OC) and elemental carbon (EC) were evaluated by carbon aerosol analyzer (IMPROVE-TOR) method. Thirteen trace elements including Al, Ba, K, Cu, Cr, Fe, Mg, Mn, Na, Ni, Ti, Pb, and Zn were evaluated by ICP-OES. Annual average TSP and PM_0.1 mass concentrations were determined to be 58.3 ± 7.8 and 10.4 ± 1.2 µg/m³, respectively. The highest levels of PM occurred in the wet season with the corresponding values for the dry seasons being lower. The averaged OC/EC ratio ranged from 3.8–4.2 (TSP) and 2.5–2.7 (PM_0.1). The char to soot ratios were constantly less than 1.0 for both TSP and PM_0.1, indicating that land transportation is the main emission source. A principal component analysis (PCA) revealed that road transportation, industry, and biomass burning are the key sources of these particles. However, PM arising from Indonesian peatland fires causes an increase in the carbon and trace element concentrations in southern Thailand. The findings make useful information for air quality management and strategies for controlling this problem, based on a source apportionment analysis. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Air Pollution)

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Graphical abstract

13 pages, 2069 KiB

Open AccessArticle

Factors Influencing PM_2.5 Concentrations in the Beijing–Tianjin–Hebei Urban Agglomeration Using a Geographical and Temporal Weighted Regression Model

by Qiuying Li, Xiaochun Li and Hongtao Li

Atmosphere 2022, 13(3), 407; https://doi.org/10.3390/atmos13030407 - 2 Mar 2022

Cited by 6 | Viewed by 2441

Abstract

Air pollution is the environmental issue of greatest concern in China, especially the PM_2.5 pollution in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). Based on sustainable development, it is of interest to study the spatiotemporal distribution of PM_2.5 and its influencing mechanisms. This study reveals the temporal evolution and spatial clustering characteristic of PM_2.5 pollution from 2015 to 2019, and quantifies the drivers of its natural and socioeconomic factors on it by using a geographical temporal weighted regression model. Results show that PM_2.5 concentrations reached their highest level in 2015 before decreasing in the following years. The monthly averages all present a U-shaped change trend. Relative to the traditional high concentrations in the northern part of the BTHUA domain in 2015, the gap in pollution between the north and south has reduced since 2018. The obvious spatial heterogeneity was demonstrated in both the strength and direction of the variables. This study may help identify reasons for high PM_2.5 concentrations and suggest appropriate targeted control and prevention measures. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Air Pollution)

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15 pages, 4458 KiB

Open AccessArticle

Statistical Analysis of PM₁₀ Concentration in the Monterrey Metropolitan Area, Mexico (2010–2018)

by Mario A. Aguirre-López, Miguel Angel Rodríguez-González, Roberto Soto-Villalobos, Laura Elena Gómez-Sánchez, Ángela Gabriela Benavides-Ríos, Francisco Gerardo Benavides-Bravo, Otoniel Walle-García and María Gricelda Pamanés-Aguilar

Atmosphere 2022, 13(2), 297; https://doi.org/10.3390/atmos13020297 - 9 Feb 2022

Cited by 1 | Viewed by 1655

Abstract

Air-quality monitoring and analysis are initial parts of a comprehensive strategy to prevent air pollution in cities. In such a context, statistical tools play an important role in determining the time-series trends, locating areas with high pollutant concentrations, and building predictive models. In this work, we analyzed the spatio-temporal behavior of the pollutant PM₁₀ in the Monterrey Metropolitan Area (MMA), Mexico during the period 2010–2018 by applying statistical analysis to the time series of seven environmental stations. First, we used experimental variograms and scientific visualization to determine the general trends and variability in time. Then, fractal exponents (the Hurst rescaled range and Higuchi algorithm) were used to analyze the long-term dependence of the time series and characterize the study area by correlating that dependence with the geographical parameters of each environmental station. The results suggest a linear decrease in PM₁₀ concentration, which showed an annual cyclicity. The autumn-winter period was the most polluted and the spring-summer period was the least. Furthermore, it was found that the highest average concentrations are located in the western and high-altitude zones of the MMA, and that average concentration is related in a quadratic way to the Hurst and Higuchi exponents, which in turn are related to some geographic parameters. Therefore, in addition to the results for the MMA, the present paper shows three practical statistical methods for analyzing the spatio-temporal behavior of air quality. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Air Pollution)

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