pm2.5 concentration
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2022 ◽  
Vol 9 ◽  
Weiwei Shi ◽  
Lin Zhang

Since the reform and opening up, China’s rapid economic growth mainly depends on the industrial development mode of “high energy consumption and high pollution,” which has caused serious haze pollution. In order to achieve the goal of haze control and sustainable development, we need to give full play to the role of technological innovation. Empirical analysis of the haze control effect of technological innovation has theoretical significance and practical value. Based on the panel data of 30 provinces in China from 2005 to 2018 and the PM2.5 concentration data published by the atmospheric composition analysis group of Dalhousie University, this study selects R&D personnel input and technology market turnover to represent the level of technological innovation and uses the panel data model, threshold effect model, and spatial Durbin model to empirically analyze the impact of technological innovation on haze pollution control. The empirical results show that 1) technological innovation can significantly reduce the PM2.5 concentration of the province, showing a positive haze control effect; 2) technological innovation indicates a negative indirect effect on PM2.5 concentration, confirming the “technology spillover effect,” that is, technological innovation also has a haze control effect on the surrounding provinces; 3) with the increase in the province’s economic aggregate, the haze control effect of technological innovation shows a trend of “high low high,” and the role of technological innovation is the lowest in the stage of economic transformation; and 4) from the perspective of regional differentiation, the haze control effect of technological innovation is the largest in the central region, and the smallest in the western region. Technological innovation indicates a positive haze control effect on all regions at all stages of economic development. This study provides policy suggestions for the government and enterprises to use innovation for cleaner production and sustainable development.

2022 ◽  
Ashwini Sankar ◽  
Andrew Goodkind ◽  
Jay Coggins

Abstract Chronic exposure to ambient fine particulate matter (PM2.5) represents one of the largest global public health risks, leading to millions of premature deaths annually. For a country facing high and spatially variable exposures, prioritizing where to reduce PM2.5 concentrations leads to an inherent tradeoff between saving the most lives and reducing inequality of exposure. This tradeoff results from the shape of the concentration-response function between exposure to PM2.5 and mortality, which indicates that the additional lives saved per unit reduction in PM2.5 declines as concentrations increase. We estimate this concentration-response function for urban areas of India, finding that a 10 unit reduction in PM2.5 in already-clean locations will reduce the mortality rate substantially (4.2% for a reduction from 30 to 20 µgm-3), while a 10 unit reduction in the dirtiest locations will reduce mortality only modestly (1.2% for a reduction from 90 to 80 µgm-3). We explore the implications of this PM2.5/mortality relationship by considering a thought experiment. If India had a fixed amount of resources to devote to PM2.5 concentration reductions across urban areas, what is the lives saved/inequality of exposure tradeoff from three different methods of employing those resources? Across our three scenarios—1) which reduces exposures for the dirtiest districts, 2) which reduces exposures everywhere equally, and 3) which reduces exposures to save the most lives—scenario 1 saves 18,000 lives per year while reducing the inequality of exposure by 65%, while scenario 3 saves 126,000 lives per year, but increases inequality by 19%.

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 46
Obuks Augustine Ejohwomu ◽  
Olakekan Shamsideen Oshodi ◽  
Majeed Oladokun ◽  
Oyegoke Teslim Bukoye ◽  
Nwabueze Emekwuru ◽  

Exposure of humans to high concentrations of PM2.5 has adverse effects on their health. Researchers estimate that exposure to particulate matter from fossil fuel emissions accounted for 18% of deaths in 2018—a challenge policymakers argue is being exacerbated by the increase in the number of extreme weather events and rapid urbanization as they tinker with strategies for reducing air pollutants. Drawing on a number of ensemble machine learning methods that have emerged as a result of advancements in data science, this study examines the effectiveness of using ensemble models for forecasting the concentrations of air pollutants, using PM2.5 as a representative case. A comprehensive evaluation of the ensemble methods was carried out by comparing their predictive performance with that of other standalone algorithms. The findings suggest that hybrid models provide useful tools for PM2.5 concentration forecasting. The developed models show that machine learning models are efficient in predicting air particulate concentrations, and can be used for air pollution forecasting. This study also provides insights into how climatic factors influence the concentrations of pollutants found in the air.

2022 ◽  
Vol 71 (2) ◽  
pp. 3051-3068
Muhammad Zulqarnain ◽  
Rozaida Ghazali ◽  
Habib Shah ◽  
Lokman Hakim Ismail ◽  
Abdullah Alsheddy ◽  

Chang Yan ◽  
Guangming Shi ◽  
Fumo Yang

Abstract Due to the heterogeneity of PM2.5 and population distribution, the representativeness of existing monitoring sites is questionable when the monitored data were used to assess the population exposure. By comparing the PM2.5 concentration from a satellite-based dataset named the China High Air Pollutants (CHAP), population and exposure level in urban areas with monitoring stations (UWS) and without monitoring stations (UNS), we discussed the rationality of the current spatial coverage of monitoring stations in eastern China. Through an analysis of air pollution in all urban areas of 256 prefectural-level municipalities in eastern China, we found that the average PM2.5 concentration in UNS in 2015 and 2018 were 52.26 μg/m3 and 41.32 μg/m3, respectively, which were slightly lower than that in UWS (52.98 μg/m3 and 41.48 μg/m3). About 12.1% of the prefectural-level municipalities had higher exposure levels in certain UNS than those in UWS. With the faster growth of UNS population, the gap between exposure levels of UNS and UWS were narrowing. Hence, currently prevalent administration-based principle of site location selection might have higher risk of missing the non-capital urban areas with relatively higher PM2.5 exposure level in the future.

2021 ◽  
Vol 12 (1) ◽  
pp. 71
Peng-Yeng Yin ◽  
Ray-I Chang ◽  
Rong-Fuh Day ◽  
Yen-Cheng Lin ◽  
Ching-Yuan Hu

The rapid development of industrialization and urbanization has had a substantial impact on the increasing air pollution in many populated cities around the globe. Intensive research has shown that ambient aerosols, especially the fine particulate matter PM2.5, are highly correlated with human respiratory diseases. It is critical to analyze, forecast, and mitigate PM2.5 concentrations. One of the typical meteorological phenomena seducing PM2.5 concentrations to accumulate is temperature inversion which forms a warm-air cap to blockade the surface pollutants from dissipating. This paper analyzes the meteorological patterns which coincide with temperature inversion and proposes two machine learning classifiers for temperature inversion classification. A separate multivariate regression model is trained for the class with or without manifesting temperature inversion phenomena, in order to improve PM2.5 forecasting performance. We chose Puli township as the studied site, which is a basin city easily trapping PM2.5 concentrations. The experimental results with the dataset spanning from 1 January 2016 to 31 December 2019 show that the proposed temperature inversion classifiers exhibit satisfactory performance in F1-Score, and the regression models trained from the classified datasets can significantly improve the PM2.5 concentration forecast as compared to the model using a single dataset without considering the temperature inversion factor.

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