Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China

Feng, Rui; Zheng, Huijun; Zhang, An-ran; Huang, Chong; Gao, Han; Ma, Yucheng

doi:10.1016/j.envpol.2019.05.101

Cited by 49 publications

(15 citation statements)

References 126 publications

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“…Similarly, the growth of urbanization in the emerging economies leads to high-energy consumption and reduces the capacity of the ecosystem (Ali et al, 2019). Urbanization affects the ecosystem in different channels; due to urbanization and industrialization, more building and factories are setup in this area, which outplay the waste absorption capacity of ecosystem (Feng et al, 2019). Urbanization also affects the environment by use of personal vehicle for transport, traffic congestions, unhygienic water and sanitation, health service Douglas, 2002;Jaysawal & Saha, 2014;Ross & Poungsomlee, 1995;Sahoo & Sethi, 2020Satterthwaite, 1993).…”

Section: Review Of Literaturementioning

confidence: 99%

The dynamic impact of urbanization, structural transformation, and technological innovation on ecological footprint and PM2.5: evidence from newly industrialized countries

Sahoo

Sethi

2021

Environ Dev Sustain

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The rapid increases in ecological footprint and air pollution have followed the fast expansion of the global economy. Urbanization has aggravated environmental strain because of population surge, but the development in the degree of technological innovation would counter balance this negative effect. Hence, in this paper we examine the dynamic impact of urbanization, economic structure, technological innovation and population density on ecological footprint and air quality (PM2.5) in Newly Industrialized Countries (NICs) from 1990 to 2017. The study uses other variables like population density, energy consumption, and life expectancy to find out the long-run relationship among variables. We apply Westerlund co-integration, Mean Group (MG) and Pooled Mean Group (PMG) technique to ascertain the long-run and short-run associations among the variables. For robustness check, we use Augmented Mean Group (AMG) and Common Correlated Mean Group (CCEMG) approach. This study would be the first study in case 10 NICs considered the determinants of ecological footprint and PM2.5. The results reveal that economic growth increases ecological footprint in NICs in long run. Similarly, coefficient of industrialization is positive and significantly related to ecological footprint. However, the service sector shows a negative relation with ecological footprint in NICs. It means service sector helps to improve the environmental quality in long run. Population density, urbanization, energy consumption and life expectancy indicate a positive effect on ecological footprint. Further, in case of PM2.5, the results suggest that economic growth and industrial sectors have high magnitude effects on the PM2.5 than the other variables. The service sector reduces PM2.5, whereas the coefficient value is not significant, but agriculture sector positively influences PM2.5 in NICs. Similarly, population density and urbanization contribution to PM2.5 are positive and significant. Hence, these NICs countries should focus more on the investment in the renewable energy sector and make stringent environmental policy for protecting the nations from environmental issues.

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Section: Review Of Literaturementioning

confidence: 99%

The dynamic impact of urbanization, structural transformation, and technological innovation on ecological footprint and PM2.5: evidence from newly industrialized countries

Sahoo

Sethi

2021

Environ Dev Sustain

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“…Under atmospheric conditions the depletion of ozone by NO x is more important than its production ( Sbai et al, 2021 ). The titration effect mainly affects the daily depletion of ozone ( Anshika et al, 2021 ; Feng et al, 2019 ; Kumar et al, 2010 ; Reddy et al, 2010 ; Wang et al, 2020 ). For diurnal variations, the maximum value of O x appeared at 5 p.m. during all pre-lockdown, lockdown, and post-lockdown periods.…”

Section: Resultsmentioning

confidence: 99%

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China

Yin

Liu

et al. 2021

Environmental Pollution

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To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 – Apr. 8, 2020), PM 2.5 , PM 10 , NO 2 , SO 2 , and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015–2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NO x at least 43 %. However, O 3 increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 – Feb. 14, 2020), which likely reduced the production of O 3 , O 3 concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NO x reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O 3 concentrations, even if reaching the limit, and VOC-specific measures should also be taken.

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“…Like automobile exhaust, cooking fumes are also a major source of air pollution [53]. The emission inventory performed by Jin et al [54] showed that in 2017, the catering industry in China released approximately 38.2 kt PM 2.5 and 47.8 kt PM 10 . In this study, at 8:00 on non-working days in winter, the catering POIs explained 5.5% of the model.…”

Section: Discussionmentioning

confidence: 99%

“…Various modeling methods, such as spatial interpolation [ 6 ], the atmospheric dispersion model [ 7 ], satellite inversion [ 8 ], and deep learning [ 9 , 10 , 11 ], have been used to simulate the distribution of regional pollutants. However, many methods, such as diffusion models, require a large amount of data difficult to obtain, and it is difficult to achieve high-precision under the condition of a lack of input data resources [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM2.5 Distribution in Beijing, China

Zhang

Cheng

Huang

et al. 2021

IJERPH

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PM2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R2 of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.

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Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China

Cited by 49 publications

References 126 publications

The dynamic impact of urbanization, structural transformation, and technological innovation on ecological footprint and PM2.5: evidence from newly industrialized countries

The dynamic impact of urbanization, structural transformation, and technological innovation on ecological footprint and PM2.5: evidence from newly industrialized countries

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM2.5 Distribution in Beijing, China

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