Dataset Information

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM_2.5 and O₃ in Wuhan, China.

ABSTRACT: To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockdown period (Jan. 23 - Apr. 8, 2020), PM_2.5, PM₁₀, NO₂, SO₂, and CO concentrations decreased significantly by 45 %, 49 %, 56 %, 39 %, and 18 % compared with the corresponding period in 2015-2019, with contributions by S(meteos) of 15 %, 17 %, 13 %, 10 %, and 6 %. This indicates an emission reduction of NO_x at least 43 %. However, O₃ increased by 43 % with a contribution by S(meteos) of 6 %. In spite of the reduced volatile organic compound (VOC) emissions by 30 % during the strict lockdown period (Jan. 23 - Feb. 14, 2020), which likely reduced the production of O₃, O₃ concentrations increased due to a weakening of the titration effect of NO. Our results suggest that conventional emission reduction (NO_x reduction only) measures may not be sufficient to reduce (or even lead to an increase of) surface O₃ concentrations, even if reaching the limit, and VOC-specific measures should also be taken.

SUBMITTER: Yin H

PROVIDER: S-EPMC8326756 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM<sub>2.5</sub> and O<sub>3</sub> in Wuhan, China.

Yin Hao H Liu Cheng C Hu Qihou Q Liu Ting T Wang Shuntian S Gao Meng M Xu Shiqi S Zhang Chengxin C Su Wenjing W

Environmental pollution (Barking, Essex : 1987) 20210802

To prevent the spread of the COVID-19 epidemic, the Chinese megacity Wuhan has taken emergent lockdown measures starting on January 23, 2020. This provided a natural experiment to investigate the response of air quality to such emission reductions. Here, we decoupled the influence of meteorological and non-meteorological factors on main air pollutants using generalized additive models (GAMs), driven by data from the China National Environmental Monitoring Center (CNEMC) network. During the lockd ...[more]

PMID: 34358865

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Project description:BackgroundMuch of the current evidence of associations between long-term PM2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m3 reduction in PM2.5 was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979-1983 averages of PM2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM2.5 in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM2.5 with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data.MethodsWe used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999-2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979-1983 and 1999-2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders.ResultsA 10 μg/m3 decrease in modeled PM2.5 based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals.ConclusionsOur approach for estimating population- and spatially-representative PM2.5 concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.

Project description:Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 μg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 μg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 μg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 μg m-3 in 2011 and PM2.5 improvements of about 2 μg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

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Project description:A sampling methodology and a mathematical data treatment were developed that enable to determine not only total suspended particulates (TSP) emitted at channeled sources but also the PM10, PM2.5, and PM1 mass fractions (w10, w2.5, and w1) and emission factors (E.F.), using a seven-stage cascade impactor. Moreover, a chemical analysis was performed to identify the elements present in these emissions. The proposed methodology was applied to different stages of the ceramic process, including ambient temperature (milling, shaping, glazing) and medium-high-temperature (spray-drying, drying, firing, and frit melting) stages. In total, more than 100 measurements were performed (pilot scale and industrial scale), which leads to a measurement time of 1500 h. Related to the mass fractions, in general, the mean values of w10 after the fabric filters operated at high performance are high and with little dispersion (75-85%), and it is also observed that they are practically independent of the stage considered, i.e., they are not significantly dependent on the initial PSD of the stream to be treated. In the case of the fine fraction w2.5, the behavior is more complex (w2.5: 30-60%), probably because the only variable is not the cleaning system, but also the nature of the processed material. Regarding abatement measures, the use of high-efficiency cleaning systems considerably reduces the emission factors obtained for fractions PM10, PM2.5, and PM1. In reference to chemical analysis, the presence of ZrO2 and Ni in the spray-drying and pressing stages, the significant concentration of ZrO2 in the glazing stage, the presence of Pb, As, and Zn in the firing stage, and the presence of Zn, Pb, Cd, and As compounds in the frits manufacturing should all be highlighted. Nevertheless, it should be pointed out that the use of some compounds, such as cadmium and lead, has been very limited in the last years and, therefore, presumably, the presence of these elements in the emissions should have been also reduced in the same way.

Dataset Information

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM_2.5 and O₃ in Wuhan, China.

Publications

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM<sub>2.5</sub> and O<sub>3</sub> in Wuhan, China.

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Dataset Information

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China.

Publications

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM<sub>2.5</sub> and O<sub>3</sub> in Wuhan, China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM_2.5 and O₃ in Wuhan, China.