scholarly journals Effects of COVID-19 lockdowns on fine particulate matter concentrations

2021 ◽  
Vol 7 (26) ◽  
pp. eabg7670
Author(s):  
Melanie S. Hammer ◽  
Aaron van Donkelaar ◽  
Randall V. Martin ◽  
Erin E. McDuffie ◽  
Alexei Lyapustin ◽  
...  
Keyword(s):  
Risk Factor ◽  
Particulate Matter ◽  
North America ◽  
Regional Differences ◽  
Fine Particulate Matter ◽  
Emission Sources ◽  
Environmental Risk Factor ◽  
Weighted Mean ◽  
Data Simulation ◽  
Fine Particulate

Lockdowns during the COVID-19 pandemic provide an unprecedented opportunity to examine the effects of human activity on air quality. The effects on fine particulate matter (PM2.5) are of particular interest, as PM2.5 is the leading environmental risk factor for mortality globally. We map global PM2.5 concentrations for January to April 2020 with a focus on China, Europe, and North America using a combination of satellite data, simulation, and ground-based observations. We examine PM2.5 concentrations during lockdown periods in 2020 compared to the same periods in 2018 to 2019. We find changes in population-weighted mean PM2.5 concentrations during the lockdowns of −11 to −15 μg/m3 across China, +1 to −2 μg/m3 across Europe, and 0 to −2 μg/m3 across North America. We explain these changes through a combination of meteorology and emission reductions, mostly due to transportation. This work demonstrates regional differences in the sensitivity of PM2.5 to emission sources.

scholarly journals Spatial analysis of air masses backward trajectories in order to identify distant sources of fine particulate matter emission / Analiza przestrzenna wstecznych trajektorii mas powietrza w celu rozpoznania odległych źródeł emisji pyłu drobnego

2015 ◽  
Vol 41 (2) ◽  
pp. 28-35 ◽  
Author(s):  
Jolanta Godłowska ◽  
Monika J. Hajto ◽  
A. Monika Tomaszewska
Keyword(s):  
Particulate Matter ◽  
Spatial Analysis ◽  
Fine Particulate Matter ◽  
Emission Sources ◽  
Air Masses ◽  
Backward Trajectories ◽  
Particulate Matter Emission ◽  
Aggregate Information ◽  
Fine Particulate ◽  
The Impact

Abstract The paper presents a method of identifying distant emission sources of fine particulate matter PM2.5 affecting significantly PM2.5 concentrations at a given location. The method involves spatial analysis of aggregate information about PM2.5 concentrations measured at the location and air masses backward trajectories calculated by HYSPLIT model. The method was examined for three locations of PM2.5 measurement stations (Diabla Góra, Gdańsk, and Katowice) which represented different environmental conditions. The backward trajectories were calculated starting from different heights (30, 50, 100 and 150 m a. g. l.). All points of a single backward trajectory were assigned to the PM2.5 concentration corresponding to the date and the site of the beginning of trajectory calculation. Daily average concentrations of PM2.5 were used, and in the case of Gdańsk also hourly ones. It enabled to assess the effectiveness of the presented method using daily averages if hourly ones were not available. Locations of distant sources of fine particulate matter emission were determined by assigning to each grid node a mean value of PM2.5 concentrations associated with the trajectories points located within the so-called search ellipse. Nearby sources of fine particulate matter emission were eliminated by filtering the trajectories points located close to each other (so-called duplicates). The analyses covered the period of January-March 2010. The results indicated the different origin of air masses in the northern and southern Poland. In Diabla Góra and Gdańsk the distant sources of fine particulate matter emission are identified in Belarus and Russia. In Katowice the impact of the Belarusian PM2.5 emission sources was also noted but as the most important fine particulate matter emission sources were considered those located in the area of Romania, Hungary, Slovakia and Ukraine.

Estimation of Contribution to PM2.5 from Ship Emissions over Korea

2020 ◽  
Author(s):  
Jihyun Seo ◽  
Nankyoung Moon
Keyword(s):  
Particulate Matter ◽  
Local Governments ◽  
Power Plants ◽  
Fine Particulate Matter ◽  
Emission Sources ◽  
Ship Emissions ◽  
Cargo Handling ◽  
Fine Particulate ◽  
Coal Power Plants ◽  
The Government

<p>In order to manage fine particulate matter, class 1 carcinogen, various policies are being prepared by the government. The government announced a set a policy measures to confront pollution issues in November 2019. Diesel cars classified as grade 5 will be banned and maximum 27 coal power plants would be plugged off from December to March when fine particulate matter usually worsen to curtail air pollution by more than 20 percent. Despite such efforts, however, it is difficult to improve the concentration of fine particulate matter. In particular, as fine particulate matter management policies are biased toward the management of coal power plants or diesel cars, port and ship emissions management are relatively insufficient.</p><p>In the case of major Korea’s port cities such as Busan and Incheon, the impacts of fine particulate matter from ship emissions are analyzed to be significant. In particular, the use of low-grade fuel such as bunker C oil, which has high sulfur content, generates a large amount of fine particulate matter and other air pollutants. As such, for fine particulate matter management in port areas, the impact of ships, cargo handling equipment and cargo trucks, which are major sources of emissions, needs to be quantitatively understood.</p><p>Under this background, the emission characteristics of ship emissions were identified by using national air pollutants emissions data in 2015, which improved the calculation method of ship emission sources and the contribution concentration of PM2.5 was analyzed using WRF and CMAQ/BFM. The modelling period is one year in 2016, and the resolution of 9km modeling was applied to Korea.</p><p>As one of the main results, the annual mean PM2.5 contribution concentration from domestic ship emission sources was analyzed to be 0.57μg/㎥, and the PM2.5 contribution concentration by local governments was calculated to be most affected by the 1.39μg/㎥ in Busan. The results of this study have not taken into account additional sources of emissions such as cargo handling equipment and cargo trucks using ports, and if this is taken into account, the actual contribution concentration of PM2.5 in port areas is expected to be higher.</p><p>The results of this research can be used as basic data when establishing policies for reducing fine particulate matter by major emission sources by local governments.</p>

scholarly journals Exposure to PM2.5 is a risk factor for acute exacerbation of surgically diagnosed idiopathic pulmonary fibrosis: a case–control study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Masahiro Tahara ◽  
Yoshihisa Fujino ◽  
Kei Yamasaki ◽  
Keishi Oda ◽  
Takashi Kido ◽  
...  
Keyword(s):  
Risk Factor ◽  
Particulate Matter ◽  
Case Control Study ◽  
Time Window ◽  
Fine Particulate Matter ◽  
Risk Exposure ◽  
Short Term ◽  
Fine Particulate ◽  
Short Term Exposure ◽  
Control Study

Abstract Background Short-term exposure to ozone and nitrogen dioxide is a risk factor for acute exacerbation (AE) of idiopathic pulmonary fibrosis (AE-IPF). The comprehensive roles of exposure to fine particulate matter in AE-IPF remain unclear. We aim to investigate the association of short-term exposure to fine particulate matter with the incidence of AE-IPF and to determine the exposure-risk time window during 3 months before the diagnosis of AE-IPF. Methods IPF patients were retrospectively identified from the nationwide registry in Japan. We conducted a case–control study to assess the correlation between AE-IPF incidence and short-term exposure to eight air pollutants, including particulate matter < 2.5 µm (PM2.5). In the time-series data, we compared monthly mean exposure concentrations between months with AE (case months) and those without AE (control months). We used multilevel mixed-effects logistic regression models to consider individual and institutional-level variables, and also adjusted these models for several covariates, including temperature and humidity. An additional analysis with different monthly lag periods was conducted to determine the risk-exposure time window for 3 months before the diagnosis of AE-IPF. Results Overall, 152 patients with surgically diagnosed IPF were analyzed. AE-IPF was significantly associated with an increased mean exposure level of nitric oxide (NO) and PM2.5 30 days prior to AE diagnosis. Adjusted odds ratio (OR) with a 10 unit increase in NO was 1.46 [95% confidence interval (CI) 1.11–1.93], and PM2.5 was 2.56 (95% CI 1.27–5.15). Additional analysis revealed that AE-IPF was associated with exposure to NO during the lag periods lag 1, lag 2, lag 1–2, and lag 1–3, and PM2.5 during the lag periods lag 1 and lag 1–2. Conclusions Our results show that PM2.5 is a risk factor for AE-IPF, and the risk-exposure time window related to AE-IPF may lie within 1–2 months before the AE diagnosis. Further investigation is needed on the novel findings regarding the exposure to NO and AE-IPF.

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