Review of land use specific source contributions in PM2.5 concentration in urban areas in India

Goyal, Prachi; Gulia, Sunil; Goyal, Sanjeev

doi:10.1007/s11869-020-00972-x

Cited by 8 publications

(2 citation statements)

References 80 publications

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“…Studying the influence of land use on PM 2.5 means understanding the formation mechanism of this pollutant from a systemic perspective, to guide the rational development, use, and protection of territorial space. In addition, it can also predict and simulate the spatial distribution based on the quantitative study of the relationship between land use and PM 2.5 [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Estimating PM2.5 Concentrations Using an Improved Land Use Regression Model in Zhejiang, China

Zheng

Zhang

2022

Atmosphere

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Fine particulate matter (PM2.5) pollution affects the environment and poses threat to human health. The study of the influence of land use and other factors on PM2.5 is crucial for the rational development and utilization of territorial space. To explore the intrinsic mechanism between PM2.5 pollution and related factors, this study used the land use regression (LUR) model, and introduced geographically weighted regression (GWR), and random forest (RF) to optimize the basic LUR model. The basic LUR model was constructed to predict the annual average PM2.5 concentrations using three elements: artificial surfaces, forest land, and wind speed as explanatory variables, with adjusted R2 of 0.645. The improved LUR models based on GWR and RF, with an adjusted R2 of 0.767 and 0.821, respectively, show better fitting effects. The LUR simulation results show that the PM2.5 pollution in the northern Zhejiang is more serious and concentrated. The concentrations are also higher in regions such as the river valley plains in central Zhejiang and the coastal plains in southeastern Zhejiang. These findings show that pollution emissions should be further reduced and environmental protection should be strengthened.

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Section: Introductionmentioning

confidence: 99%

Estimating PM2.5 Concentrations Using an Improved Land Use Regression Model in Zhejiang, China

Zheng

Zhang

2022

Atmosphere

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“…Anthropogenic activities contributing to local and regional air pollution include the consumption of fossil fuels for power generation, transportation, and heating/ cooling, agricultural activities -particularly widespread burning of crop residues [15] , and atmospheric emissions from many types of industrial manufacturing plants [16] . Different human activities and land uses at different times of the year contribute to seasonal fluctuations in air pollutants in many regions [17] , although their monitoring tends to focus on their impacts to urban areas. However, natural global processes associated with storms, including dust storms [18,19] , complex atmospheric circulation patterns and other weather phenomena, such as seasonal mixing layer heights and inversions [20] , are also responsible for the input, distribution and movement of certain pollutants in the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

Wood

2021

Res. in Ecol.

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Medium-term air quality assessment, benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes. By using daily and monthly averaged data, medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives. By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark (CLAB). The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator. Such a benchmark can provide an overall air quality assessment for areas of interest. A case study is presented for Dallas County (U.S.A.) applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019. Six air pollutants considered are: ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, benzene and particulate matter less than 2.5 micrometres. These pollutants are assessed individually and in terms of CLAB, and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019. Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years. The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs. Conducting CLAB analysis on an ongoing basis, using a relevant nearpast time interval for benchmarking that covers several years, can reveal useful monthly, seasonal and annual trends in overall air quality. This type of medium-term, benchmarked air quality data analysis is well suited for ecosystem monitoring.

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Critical Analysis of PM2.5 in Delhi Region to Strategize Effective Air Pollution Management Plan

Jha,

Suman,

Mishra

2024

J. Inst. Eng. India Ser. A

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Review of land use specific source contributions in PM2.5 concentration in urban areas in India

Cited by 8 publications

References 80 publications

Estimating PM2.5 Concentrations Using an Improved Land Use Regression Model in Zhejiang, China

Estimating PM2.5 Concentrations Using an Improved Land Use Regression Model in Zhejiang, China

Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

Critical Analysis of PM2.5 in Delhi Region to Strategize Effective Air Pollution Management Plan

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