Effect of PM2.5 chemical constituents on atmospheric visibility impairment

Khanna, Isha; Khare, Mukesh; Gargava, Prashant; Khan, Anwar A.

doi:10.1080/10962247.2018.1425772

Cited by 44 publications

(14 citation statements)

References 30 publications

(29 reference statements)

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“…A surrogate model is a simple model (usually statistical) which can map the inputs to the outputs of the simulation model with sufficiently good accuracy, given the same inputs. In this study, we choose a type of surrogate model called a Gaussian process emulator, which works like a function for multidimensional interpolation and has been used extensively in many areas of applied science (Carslaw et al, 2013;Koehler and Owen, 1996;Queipo et al, 2005;Vanuytrecht and Willems, 2014;Vu et al, 2015;Degroote et al, 2012) and uncertainty assessments of atmospheric models (Lee et al, 2011(Lee et al, , 2012(Lee et al, , 2016. Gaussian process emulators typically require a relatively small number of runs of the computationally expensive model to generate; this is in contrast to other surrogate modelling approaches, such as neural networks, which typically require thousands of model runs to train them.…”

Section: Global Sensitivity Analysis Of Urban Air Pollutionmentioning

confidence: 99%

Mitigation of PM2.5 and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

et al. 2020

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Abstract. Fine particulate matter (PM2.5) and surface ozone (O3) are major air pollutants in megacities such as Delhi, but the design of suitable mitigation strategies is challenging. Some strategies for reducing PM2.5 may have the notable side effect of increasing O3. Here, we demonstrate a numerical framework for investigating the impacts of mitigation strategies on both PM2.5 and O3 in Delhi. We use Gaussian process emulation to generate a computationally efficient surrogate for a regional air quality model (WRF-Chem). This allows us to perform global sensitivity analysis to identify the major sources of air pollution and to generate emission-sector-based pollutant response surfaces to inform mitigation policy development. Based on more than 100 000 emulation runs during the pre-monsoon period (peak O3 season), our global sensitivity analysis shows that local traffic emissions from the Delhi city region and regional transport of pollution emitted from the National Capital Region (NCR) surrounding Delhi are dominant factors influencing PM2.5 and O3 in Delhi. They together govern the O3 peak and PM2.5 concentration during daytime. Regional transport contributes about 80% of the PM2.5 variation during the night. Reducing traffic emissions in Delhi alone (e.g. by 50 %) would reduce PM2.5 by 15 %–20 % but lead to a 20 %–25 % increase in O3. However, we show that reducing NCR regional emissions by 25 %–30 % at the same time would further reduce PM2.5 by 5 %–10 % in Delhi and avoid the O3 increase. This study provides scientific evidence to support the need for joint coordination of controls on local and regional scales to achieve effective reduction in PM2.5 whilst minimising the risk of O3 increase in Delhi.

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Section: Global Sensitivity Analysis Of Urban Air Pollutionmentioning

confidence: 99%

Mitigation of PM2.5 and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

et al. 2020

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“…However, an average person breathes at a height of 1.5 m above the ground (Kenagy et al, 2016), but little interest has been shown in the particles near the 1.5 m above ground surface. The study on constituent changes in particles is also very important, as the constituent analysis could help to track contributors of PM 2.5 (Khanna et al, 2018). Another reason for clarifying particle constituent is that inhaled particulate matter can have negative effects on respiratory and cardiovascular health, and can even damage DNA (Hong et al, 2002;Lelieveld & Poschl, 2017).…”

Section: Introductionmentioning

confidence: 99%

Understanding PM_2.5 concentration and removal efficiency variation in urban forest park—Observation at human breathing height

Yan

et al. 2020

PeerJ

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To increase our knowledge of PM2.5 concentrations near the surface in a forest park in Beijing, an observational study measured the concentration and composition of PM2.5 in Beijing Olympic Forest Park from 2014 to 2015. This study analyzed the meteorological factors and removal efficiency at 1.5 m above the ground (human breathing height) over the day in the forest. The results showed that the average concentrations of PM2.5 near the surface peaked at 07:00–09:30 and reached their lowest at 12:00–15:00. Besides, the results showed that the annual concentration of PM2.5 in the forest was highest during winter, followed by spring and fall, and was lowest during summer. The main chemical components of PM2.5 near the surface in the forest were SO42− and NO3−, which accounted for 68.72% of all water-soluble ions that we observed. The concentration of PM2.5 in the forest had a significant positive correlation with relative humidity and a significant negative correlation with temperature. The removal efficiency near the surface showed no significant variation through the day or year. In the forest, the highest removal efficiency occurred between 07:00 and 09:30 in summer, while the lowest occurred between 09:30 and 12:00 in winter.

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“…This serious air pollution issue not only restricts the sustainable development of the regional economy but also brings negative influences on human health [6]. Particulate matter (PM), a major component of atmospheric pollutants, has received much attention in the field of atmospheric environment research [7][8][9]. There are various sources of PM emission, of which fugitive dust source is one of the most common sources in urban areas [10].…”

Section: Introductionmentioning

confidence: 99%

Estimating Particulate Matter Emission from Dust Sources Using ZY-3 Data and GIS Technology—A Case Study in Zhengzhou City, China

Yang

Song

et al. 2021

Atmosphere

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With the rapid development of the social economy in China, numerous Chinese cities are facing high levels of particulate matter (PM) pollution problems. In this study, high-resolution ZY-3 images and GIS techniques were used to establish the emission inventory of total suspended particle (TSP), particulate matter 10 (PM10) and particulate matter 2.5 (PM2.5) from fugitive dust sources in May 2016, and a spatial grid of 3 km × 3 km resolution was established to demonstrate the spatial distribution of PM emission. Results showed that the total emissions of TSP, PM10 and PM2.5 in Zhengzhou city were 237.5 kt·a−1, 103.7 kt·a−1 and 22.4 kt·a−1, respectively. Construction dust source was the main fugitive dust emission source in Zhengzhou city—the TSP, PM10 and PM2.5 emission of which account for 76.42%, 89.68% and 88.39%, respectively, of the total emission, followed by road dust source and soil dust source. PM emission was higher in Zhongyuan, Huiji, Jinshui and Zhengdong New District, while Zhongmou, Xingyang, Dengfeng and other remote areas had low PM emissions. Compared to other Chinese cities or regions, the PM emission from the construction dust source was at a high level in Zhengzhou city, while the PM emissions from the soil dust source and road dust source were at moderate levels.

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Effect of PM_2.5 chemical constituents on atmospheric visibility impairment

Abstract: The chemical constituents of PM that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.

Cited by 44 publications

References 30 publications

Mitigation of PM<sub>2.5</sub> and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

Mitigation of PM<sub>2.5</sub> and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

Understanding PM_2.5 concentration and removal efficiency variation in urban forest park—Observation at human breathing height

Estimating Particulate Matter Emission from Dust Sources Using ZY-3 Data and GIS Technology—A Case Study in Zhengzhou City, China

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Effect of PM2.5 chemical constituents on atmospheric visibility impairment

Abstract: The chemical constituents of PM that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.

Cited by 44 publications

References 30 publications

Mitigation of PM&lt;sub&gt;2.5&lt;/sub&gt; and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

Mitigation of PM&lt;sub&gt;2.5&lt;/sub&gt; and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

Understanding PM2.5 concentration and removal efficiency variation in urban forest park—Observation at human breathing height

Estimating Particulate Matter Emission from Dust Sources Using ZY-3 Data and GIS Technology—A Case Study in Zhengzhou City, China

Contact Info

Product

Resources

About

Effect of PM_2.5 chemical constituents on atmospheric visibility impairment

Mitigation of PM<sub>2.5</sub> and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

Mitigation of PM<sub>2.5</sub> and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period

Understanding PM_2.5 concentration and removal efficiency variation in urban forest park—Observation at human breathing height