Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China

Yang, Shan; Wu, Haitian; Chen, Jian; Lin, Xintao; Lü, Ting

doi:10.3390/atmos9020047

Cited by 18 publications

(6 citation statements)

References 43 publications

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“…Urbanization, industrialization, and other human activities, coupled with climate change, have greatly affected air quality [1][2][3][4][5]. Fine particulate matter with an aerodynamic diameter of less than 2.5 µm, known as PM 2.5 , may be suspended in the atmosphere for a considerable period and can be transported over long distances [6].…”

Section: Introductionmentioning

confidence: 99%

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Meadows

et al. 2021

Remote Sensing

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Fine particulate matter in the lower atmosphere (PM2.5) continues to be a major public health problem globally. Identifying the key contributors to PM2.5 pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM2.5 values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM2.5 in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM2.5 varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) > socioeconomic factors (e.g., gross domestic product, GDP) > topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM2.5 concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM2.5 concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM2.5 values and offers a reliable reference for pollution control strategies.

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Section: Introductionmentioning

confidence: 99%

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Meadows

et al. 2021

Remote Sensing

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“…The outcomes of Mukherjee and Agrawal ( 2018 ) are similar to current research work. Yang et al ( 2018 ) explored the correlation between PM 2.5 concentrations and the landscape pattern index. Zaman et al ( 2021 ) stated that there is no direct association between PM 2.5 and AOD, while Sinha et al ( 2015 ) exhibited that they have a strong correlation.…”

Section: Discussionmentioning

confidence: 99%

“…Besides its health effects, it is a major threat to climatic change, socio-economics, and biodiversity. PM 2.5 can absorb serious toxic contaminants like heavy metals, volatile organic compounds, and polycyclic aromatic hydrocarbons, which can cause respiratory, pulmonary, and mutagenic damage (Yang et al 2018). Long-term exposure to atmospheric particles, particularly PM 2.5 and PM 10 (diameter ≤ 10 μm) has constantly shown the harmful effects of different epidemiological studies on human health, including cardiovascular, respiratory, infectious, and cancer diseases and premature mortality (Chen et al 2018;).…”

Section: Introductionmentioning

confidence: 99%

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

Islam

Awadh

Mallick

et al. 2023

Air Qual Atmos Health

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Fine particulate matter (PM 2.5 ) has become a prominent pollutant due to rapid economic development, urbanization, industrialization, and transport activities, which has serious adverse effects on human health and the environment. Many studies have employed traditional statistical models and remote-sensing technologies to estimate PM 2.5 concentrations. However, statistical models have shown inconsistency in PM 2.5 concentration predictions, while machine learning algorithms have excellent predictive capacity, but little research has been done on the complementary advantages of diverse approaches. The present study proposed the best subset regression model and machine learning approaches, including random tree, additive regression, reduced error pruning tree, and random subspace, to estimate the ground-level PM 2.5 concentrations over Dhaka. This study used advanced machine learning algorithms to measure the effects of meteorological factors and air pollutants (NO X , SO 2 , CO, and O 3 ) on the dynamics of PM 2.5 in Dhaka from 2012 to 2020. Results showed that the best subset regression model was well-performed for forecasting PM 2.5 concentrations for all sites based on the integration of precipitation, relative humidity, temperature, wind speed, SO 2 , NO X , and O 3 . Precipitation, relative humidity, and temperature have negative correlations with PM 2.5 . The concentration levels of pollutants are much higher at the beginning and end of the year. Random subspace is the optimal model for estimating PM 2.5 because it has the least statistical error metrics compared to other models. This study suggests ensemble learning models to estimate PM 2.5 concentrations. This study will help quantify ground-level PM 2.5 concentration exposure and recommend regional government actions to prevent and regulate PM 2.5 air pollution. Supplementary Information The online version contains supplementary material available at 10.1007/s11869-023-01329-w.

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“…Obvious differences in PM 2.5 levels are found across urban land cover patterns [10]. In addition, urban landscape patterns or structures, including the composition and configuration of built environments, have aroused interest by using a series of relevant metrics to investigate their effects on PM 2.5 [11][12][13][14], although the influence of individual metrics may differ among studies. Moreover, urban morphology has attracted increasing attention because of its strong impacts on PM 2.5 .…”

Section: Introductionmentioning

confidence: 99%

PCA-Based Identification of Built Environment Factors Reducing PM2.5 Pollution in Neighborhoods of Five Chinese Megacities

Chen

Dai

2022

Atmosphere

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Air pollution, especially PM2.5 pollution, still seriously endangers the health of urban residents in China. The built environment is an important factor affecting PM2.5; however, the key factors remain unclear. Based on 37 neighborhoods located in five Chinese megacities, three relative indicators (the range, duration, and rate of change in PM2.5 concentration) at four pollution levels were calculated as dependent variables to exclude the background levels of PM2.5 in different cities. Nineteen built environment factors extracted from green space and gray space and three meteorological factors were used as independent variables. Principal component analysis was adopted to reveal the relationship between built environment factors, meteorological factors, and PM2.5. Accordingly, 24 models were built using 32 training neighborhood samples. The results showed that the adj_R2 of most models was between 0.6 and 0.8, and the highest adj_R2 was 0.813. Four principal factors were the most important factors that significantly affected the growth and reduction of PM2.5, reflecting the differences in green and gray spaces, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. Furthermore, the relative error was used to test the error of the predicted values of five verification neighborhood samples, finding that these models had a high fitting degree and can better predict the growth and reduction of PM2.5 based on these built environment factors.

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Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China

Cited by 18 publications

References 43 publications

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

PCA-Based Identification of Built Environment Factors Reducing PM2.5 Pollution in Neighborhoods of Five Chinese Megacities

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