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The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city.

ABSTRACT: Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases. The present paper is an attempt to study the twenty fog samples collected from the rooftop of an academic building of Guru Nanak Dev University, Amritsar, India from November 2017 to January 2018. Fog samples were analysed for various parameters viz. pH, electrical conductivity (EC), chloride (Cl^-), nitrate (NO₃ ^-) and sulphate (SO₄ ^2-) levels. The pH, EC, and Cl^-, NO₃ ^- and SO₄ ^2- levels in the fog samples were estimated as 6.3-7.9, 240-790 μS cm^-1, 108-2025 μeq L^-1, 105-836 μeq L^-1 and 822-5642 μeq L^-1, respectively. It was noticed that sulphate was the dominant anion in fog samples. The SO₄ ^2- to NO₃ ^- molar ratio in the fog was estimated as 7.6 which suggests the burning of fossil fuel as the major pollutant from vehicular exhausts. Multiple regression analysis was performed to evaluate the effect of PM_2.5/PM₁₀ ratio and relative humidity (RH) on visibility. A box-cox plot of power transformation produced better model fitting, employing a square root transformation of the visibility which indicated that the PM_2.5/PM₁₀ and RH have an exponential effect on visibility.

SUBMITTER: Yadav R

PROVIDER: S-EPMC8996368 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city.

Yadav Rekha R Sugha Aditi A Bhatti Manpreet S MS Kansal Sushil K SK Sharma Sudhir K SK Mandal Tuhin K TK

RSC advances 20220411 18

Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases. The present paper is an attempt to study the twenty fog samples collected from the rooftop of an academic building of Guru Nanak Dev University, Amritsar, India from November 2017 to January 2018. Fog samples were analysed for various parameters <i>viz.</i> pH, electrical conductivity (EC), chloride (Cl<sup>-</sup>), ...[more]

PMID: 35425065

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Project description:BackgroundThe absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution.ObjectiveWe constructed an ensemble machine learning model to predict daily PM2.5 concentrations for regions lack of PM2.5 observations.MethodsThe model was constructed based on daily PM2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020.ResultsAs compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM2.5 concentrations with a cross-validation R2 of 0.68. The predicted level of daily PM2.5 concentrations were consistent with previous measurements. The predicted average yearly PM2.5 concentration for the eight stations is 50.65 µg/m3. For all stations, the monthly average PM2.5 concentrations reached their maximum in July and their minimum in November.SignificanceThese findings make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations.Impact statementThe scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.

Project description:BackgroundEpigenetic age, as defined by DNA methylation, may be influenced by air pollution exposure.ObjectiveTo evaluate the relationship between NO2, particulate matter (PM), PM components and accelerated epigenetic age.MethodsIn a sample of non-Hispanic white women living in the contiguous U.S. (n = 2747), we estimated residential exposure to PM2.5, PM10 and NO2 using a model incorporating land-use regression and kriging. Predictive k-means was used to assign participants to clusters representing different PM2.5 component profiles. We measured DNA methylation (DNAm) in blood using the Illumina's Infinium HumanMethylation450 BeadChip and calculated DNAm age using the Hannum, Horvath and Levine epigenetic clocks. Age acceleration was defined based on residuals after regressing DNAm age on chronological age. We estimated associations between interquartile range (IQR) increases in pollutants and age acceleration using linear regression. For PM2.5, we stratified by cluster membership. We examined epigenome-wide associations using robust linear regression models corrected with false discovery rate q-values.ResultsNO2 was inversely associated with age acceleration using the Hannum clock (β = -0.24, 95% CI: -0.47, -0.02). No associations were observed for PM10. For PM2.5, the association with age acceleration varied by PM2.5 component cluster. For example, with the Levine clock, an IQR increase in PM2.5 was associated with an over 6-year age acceleration in a cluster that has relatively high fractions of crustal elements relative to overall PM2.5 (β = 6.57, 95% CI: 2.68, 10.47), and an almost 2-year acceleration in a cluster characterized by relatively low sulfur fractions (β = 1.88, 95% CI: 0.51, 3.25). In a cluster distinguished by lower relative nitrate concentrations, PM2.5 was inversely associated with age acceleration (β = -1.33, 95% CI: -2.43, -0.23). Across the epigenome, NO2 was associated with methylation at 2 CpG sites.ConclusionAir pollution was associated with epigenetic age, a marker of mortality and disease risk, among certain PM2.5 component profiles.

Dataset Information

The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city.

Publications

The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city.

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