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24 results for “Source apportionment”
Air pollution knowledge assessments (APnA) - PM2.5 Source Apportionment Results for 50 Indian Cities
<p>Summary of estimated source contributions to ambient PM2.5 concentrations, including the contribution of sources outside the city airsheds. These results are explained and discussed in journal articles.<br> 1. Air pollution knowledge assessments (APnA) for 20 Indian cities [<a href="https://doi.org/10.1016/j.uclim.2018.11.005">link</a>]<br> 2. National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans [<a href="https://doi.org/10.1016/j.aeaoa.2020.100096">link</a>]</p> <p>Source descriptions listed in the table<br> A = all transport (road, rail, aviation, and shipping)<br> B = residential<br> C = industries (without brick kilns)<br> D = all dust (construction and resuspension due to vehicle movement)<br> E = open waste burning<br> F = diesel generator sets<br> G = brick manufacturing<br> H = sea salt (where applicable) and<br> I = outside/regional contribution<br> <br> For more information on the model formulation and detailed results by city including summaries of monitoring data, monitoring requirements, points of interest, emission inventories, gridded emissions and pollution maps, and monthly variation in PM2.5 concentrations, visit<br> <a href="https://www.urbanemissions.info/india-apna">https://www.urbanemissions.info/india-apna</a></p>
Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi, India
<p>Delhi is one of the most polluted cities worldwide and a comprehensive understanding and deeper insight into the air pollution and its sources is of high importance. We report 5 months of highly time-resolved measurements ofnon-refractory PM2.5 and black carbon (BC). Additionally, source apportionment based on positive matrix factorization (PMF) of the organic aerosol (OA) fraction is presented. The highest pollution levels are observed during winter in December/January. During that time, also uniquely high chloride concentrations are measured, which are sometimes even themost dominant NR-species in themorning hours. With increasing temperature, the total PM2.5 concentration decreases steadily, whereas the chloride concentrations decrease sharply. The concentrationsmeasured in May are roughly 6 times lower than in December/January. PMFanalysis resolves two primary factors, namely hydrocarbon-like (traffic-related) OA (HOA) and solid fuel combustion OA (SFCOA), and one or two secondary factors depending on the season. The uncertainties of the PMF analysis are assessed by combining the random a-value approach and the bootstrap resampling technique of the PMF input. The uncertainties for the resolved factors range from ±18% to ±19% for HOA, ±7% to ±19% for SFC-OA and ±6 % to ±11% for the OOAs. The average correlation of HOA with equivalent black carbon from traffic (eBCtr)is R2 = 0.40, while SFC-OA has a correlation of R2 = 0.78 with equivalent black carbon from solid fuel combustion (eBCsf). Anthracene (m/z 178) and pyrene (m/z 202) (PAHs) are mostly explained by SFC-OA and follow its diurnal trend (R2=0.98 and R2=0.97). The secondary oxygenated aerosols are dominant during daytime. The average contribution during the afternoon hours (1 pm–5 pm) is 59% to the total OA mass, with contributions up to 96% in May. In contrast, the primary sources are more important during nighttime: the mean nightly contribution (22 pm–3 am) to the total OA mass is 48%, with contributions up to 88% during some episodes in April.</p> <p> </p> <p>This repository contains the data corresponding to the figures presented in the published main text.</p>
Modelling PM2.5 during severe atmospheric pollution episode in Lagos, Nigeria: Spatiotemporal variations, source apportionment, and meteorological influences
<p>Data</p>
Major outputs for source apportionment of fine secondary inorganic aerosol over the Pearl River Delta region using a hybrid method
<p>The results for our work can be downloaded from this URL. The source impact estimates were simulated by CAMx (v6.4) with the module of PSAT in January, April, July, and October 2015. Matlab R2017b was used to perform spatial interpolation, optimization, and statistics calculations.</p>
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OpenNeuro
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