The impact of COVID-19 on urban PM2.5 -taking Hubei Province as an example.
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ABSTRACT:
SUBMITTER: Yang K
PROVIDER: S-EPMC8660577 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The lockdown measures caused by the COVID-19 pandemic substantially affected air quality in many cities through reduced emissions from a variety of sources, including traffic. The change in PM2.5 and its chemical composition in downtown Toronto, Canada, including organic/inorganic composition and trace metals, were examined by comparing with a pre-lockdown period and respective periods in the three previous years. During the COVID-19 lockdown, the average traffic volume reduced by 58%, whereas PM2.5 only decreased by 4% relative to the baselines. Major chemical components of PM2.5, such as organic aerosol and ammonium nitrate, showed significant seasonal changes between pre- and lockdown periods. The changes in local and regional PM2.5 sources were assessed using hourly chemical composition measurements of PM2.5. Major regional and secondary PM2.5 sources exhibited no clear reductions during the lockdown period compared to pre-lockdown and the previous years. However, cooking emissions substantially dropped by approximately 61% due to the restrictions imposed on local businesses (i.e., restaurants) during the lockdown, and then gradually increased throughout the recovery periods. The reduction in non-tailpipe emissions, characterized by road dust and brake/tire dust, ranged from 37% to 61%, consistent with the changes in traffic volume and meteorology across seasons in 2020. Tailpipe emissions dropped by approximately 54% and exhibited even larger reductions during morning rush hours. The reduction of tailpipe emissions was statistically associated with the reduced number of trucks, highlighting that a small fraction of trucks contributes disproportionally to tailpipe emissions. This study provides insight into the potential for local benefits to arise from traffic intervention in traffic-dominated urban areas and supports the development of targeted strategies and regulations to effectively reduce local air pollution.
| S-EPMC8747944 | biostudies-literature
Project description:The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003-2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Biweekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio-ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites.
| S-EPMC4267573 | biostudies-literature
Project description:In 2015, we measured polycyclic aromatic hydrocarbons (PAHs) in atmospheric fine particulate matter (PM2.5) collected from 5 cities in Zhejiang Province. The mean toxic equivalent quotient (TEQ) values of benzo(a)pyrene (BaP) ranged between 1.2-3.1 ng/m3. The BaP-TEQ displayed seasonal trends, such that winter > spring and autumn > summer. During the winter, the most abundant individual PAHs were 4-6ring PAHs (84.04-91.65%). The median daily intake of atmospheric PAHs ranged between 2.0-7.4 ng/day for all populations, with seasonal trends identical to that of BaP-TEQ. The 95th incremental lifetime cancer risk (ILCR) values induced by PM2.5-bound PAHs were far lower than 10-6 for all populations. The data suggested that the pollution levels in the 5 Zhejiang Province cities were higher than the Chinese National Ambient Air Quality Standard (NAAQS). In the future, relevant measures should be taken to control atmospheric PAHs, especially 4-6 ring PAHs. The data also revealed no obvious cancer risk for populations residing in these 5 cities of Zhejiang Province.
| S-EPMC6513845 | biostudies-literature
Project description:Air pollution is becoming serious in developing country, and how to quantify the role of local emission and/or meteorological factors is very important for government to implement policy to control pollution. Here, we use a random forest model, a machine learning (ML) approach, combined with a de-weather method to analyze the PM2.5 level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM2.5 concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM2.5 concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be related to regional transport. After excluding the contribution of meteorological conditions, the PM2.5 concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 μ g/m3). Our estimates also indicate that under similar meteorological conditions as in February and March 2019, an emission reduction intensity equivalent to about 48% of the emission reduction intensity during the lockdown may bring the annual average PM2.5 concentration to the standard (35 μ g/m3). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM2.5, and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.
| S-EPMC9756305 | biostudies-literature
Project description:In China, severe haze is a major public health concern affecting residents' health and well-being. This study used hourly air quality monitoring data from 285 cities in China to analyze the effect of green coverage (GC) and other economic variables on the incremental PM2.5 concentration (ΔPM2.5) during peak hours. To detect possible non-linear and interaction effect between predictive variables, a kernel-based regularized least squares (KRLS) model was used for empirical analysis. The results show that there was considerable heterogeneity between cities regarding marginal effect of GC on ΔPM2.5, which could potentially be explained by different seasons, latitude, urban maintenance expenditure (UE), real GDP per capita (PG), and population density (PD). Also described in this study, in cities with high UE, the growth of GC, PG, and PD always remain a positive impact on mitigation of haze pollution. This shows that government expenditure on urban maintenance can reduce or mitigate the environmental pollution from economic development. In addition, the influence of other urban elements on air quality had also been analyzed so that different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.
| S-EPMC7701305 | biostudies-literature
Project description:Amid the COVID-19 pandemic, a nationwide lockdown was imposed in the United Kingdom (UK) on March 23, 2020. These sudden control measures led to radical changes in human activities in the Greater London Area (GLA). During this lockdown, transportation use was significantly reduced and non-key workers were required to work from home. This study aims to understand how population exposure to PM2.5 and NO2 changed spatially and temporally across London, in different microenvironments, following the lockdown period relative to the previous three-year average in the same calendar period. Our research shows that population exposure to NO2 declined significantly (52.3% ± 6.1%), while population exposure to PM2.5 showed a smaller relative reduction (15.7% ± 4.1%). Changes in population activity had the strongest relative influence on exposure levels during morning rush hours, when prior to the lockdown a large percentage of people would normally commute or be at the workplace. In particular, a very high exposure decrease was observed for both pollutants (approximately 66% for NO2 and 19% for PM2.5) at 08:00am, consistent with the radical changes in population commuting. The infiltration of outdoor air pollution into housing modifies the degree of exposure change both temporally and spatially. Moreover, this study shows that the impacts on air pollution exposure vary across groups with different socioeconomic status (SES), with a disproportionate positive effect on the areas of the city home to more economically deprived communities.
| S-EPMC9750168 | biostudies-literature
Project description:The Central Plains Urban Agglomeration (CPUA) is the largest region in central China and suffers from serious air pollution. To reveal the spatiotemporal variations and the sources of fine particulate matter (PM2.5, with an aerodynamic diameter of smaller than 2.5 μm) concentrations of CPUA, multiple and transdisciplinary methods were used to analyse the collected millions of PM2.5 concentration data. The results showed that during 2017 ~ 2020, the yearly mean concentrations of PM2.5 for CPUA were 68.3, 61.5, 58.7, and 51.5 μg/m3, respectively. The empirical orthogonal function (EOF) analysis suggested that high PM2.5 pollution mainly occurred in winter (100.8 μg/m3, 4-year average). The diurnal change in PM2.5 concentrations varied slightly over the season. The centroid of the PM2.5 concentration moved towards the west over time. The spatial autocorrelation analysis indicated that PM2.5 concentrations exhibited a positive spatial autocorrelation in CPUA. The most polluted cities distributed in the northern CPUA (Handan was the centre) formed a high-high agglomeration, and the cities located in the southern CPUA (Xinyang was the centre) formed a low-low agglomeration. The backward trajectory model and potential source contribution function were employed to discuss the regional transportation of PM2.5. The results demonstrated that internal-region and cross-regional transport of anthropogenic emissions were all important to PM2.5 pollution of CPUA. Our study suggests that joint efforts across cities and regions are necessary.Supplementary informationThe online version contains supplementary material available at 10.1007/s11869-022-01178-z.
| S-EPMC9257121 | biostudies-literature
Project description:The reports concerning air quality published by WHO and EEA showed that 33 out of 50 cities with highest concentration of particulate matter (PM)2.5 in UE are located in Poland. Various investigations identify main health outcomes to be consistently related to PM10 and PM2.5. Increased concentration of PM is responsible for 47.3 thousands of premature deaths every year in Poland. The objective of this study was the measurement-based assessment for determining whether the concentrations of PM10 and PM2.5 are within admissible limits or exceeded in Silesia Province. The data provided by the Voivodship Inspectorate for Environmental Protection in Katowice was used in the analysis. The measurements were made in years 2009-2016 for PM2.5 and 2014-2017 for PM10 in three measurement stations: two in Katowice (capital of Silesia Province) and one in Żory. The increase in the number of excessive levels of average daily PM10 concentration in year 2017 were observed in all three measurement stations, both for the acceptable level, information and alarm level, with lack or singular excessive levels in the previous years. The increase in average annual PM2.5 concentrations in year 2016 was also observed, as compared to the previous year in all three measurement stations. The highest pollution is observed in winter. The main cause of exceeded acceptable PM concentrations in Poland is household heating systems, boilers and furnaces burnt with coal or wood, and chimneys. In Silesian Province, the air quality is poor and has deteriorated over the last year.
| S-EPMC6096885 | biostudies-literature
Project description:BackgroundAir pollution is the greatest environmental threat to human health in the world today and is responsible for an estimated 7-9 million deaths annually. One of the most damaging air pollutants is PM2.5 pollution, fine airborne particulate matter under 2.5 microns in diameter. Exposure to PM2.5 pollution can cause premature death, heart disease, lung cancer, stroke, diabetes, asthma, low birthweight, and IQ loss. To avoid these adverse health effects, the WHO recommends that PM2.5 levels not exceed 5 μg/m3.MethodsThis study estimates the negative health impacts of PM2.5 pollution in Texas in 2016. Local exposure estimates were calculated at the census tract level using the EPA's BenMAP-CE software. In BenMAP, a variety of exposure-response functions combine air pollution exposure data with population data and county-level disease and death data to estimate the number of health effects attributable to PM2.5 pollution for each census tract. The health effects investigated were mortality, low birthweight, stroke, new onset asthma, new onset Alzheimer's, and non-fatal lung cancer.FindingsThis study found that approximately 26.7 million (98.9%) of the 27.0 million people living in Texas in 2016 resided in areas where PM2.5 concentrations were above the WHO recommendation of 5 μg/m3, and that 2.6 million people (9.8%) lived in areas where the average PM2.5 concentration exceeded 10 μg/m3. This study estimates that there were 8,405 (confidence interval [CI], 5,674-11,033) premature deaths due to PM2.5 pollution in Texas in 2016, comprising 4.3% of all deaths. Statewide increases in air-pollution-related morbidity and mortality were seen for stroke (2,209 - CI: [576, 3,776]), low birthweight (2,841 - CI: [1,696, 3,925]), non-fatal lung cancers (636 - CI: [219, 980]), new onset Alzheimer's disease (24,575 - CI: [20,800, 27,540]), and new onset asthma (7,823 - CI: [7,557, 8,079]).ConclusionThis study found that air pollution poses significant risks to the health of Texans, despite the fact that pollution levels across most of the state comply with the EPA standard for PM2.5 pollution of 12 μg/m3. Improving air quality in Texas could save thousands of lives from disease, disability, and premature death.
| S-EPMC10722416 | biostudies-literature
Project description:In Dhaka, Bangladesh, fine particulate matter (PM2.5) air pollution shows strong seasonal trends, with significantly higher mean concentrations during winter than during the monsoon (winter = 178.1 μg/m3 vs. monsoon = 30.2 μg/m3). Large-scale open burning of post-harvest agricultural waste across the Indo-Gangetic Plain is a major source of PM2.5 air pollution in northern India during the non-monsoon period. This study evaluates the extent to which the seasonal differences in PM2.5 pollution concentrations in Dhaka are accounted for by biomass-burning vs. fossil-fuel combustion sources. To assess this, an index was developed based on elemental potassium (K) as a marker for biomass particulate matter, after adjusting for soil-associated K contributions. Alternatively, particulate sulfur was employed as a tracer index for fossil-fuel combustion PM2.5. By simultaneously regressing total PM2.5 on S and adjusted K, the PM2.5 mass for each day was apportioned into: 1) fossil-fuels combustion associated PM2.5; 2) biomass-burning associated PM2.5; and, 3) all other PM2.5. The results indicated that fossil-fuel combustion contributed 21.6% (19.5 μg/m3), while biomass contributed 40.2% (36.3 μg/m3) of overall average PM2.5 from September 2013 to December 2017. However, the mean source contributions varied by season: PM2.5 in Dhaka during the monsoon season was dominated by fossil-fuels sources (44.3%), whereas PM2.5 mass was dominated by biomass-burning (41.4%) during the remainder of the year. The contribution to PM2.5 and each of its source components by transport of pollution into Dhaka during non-monsoon time was also evaluated by: 1) Conditional bivariate (CBPF) and pollution rose plots; 2) Concentration weighted trajectories (CWT), and; 3) NASA satellite photos to identify aerosol loading and fire locations on high pollution days. The collective evidence indicates that, while the air pollution in Dhaka is contributed to by both local and transboundary sources, the highest pollution days were dominated by biomass-related PM2.5, during periods of crop-burning in the Indo-Gangetic Plain.
| S-EPMC9581344 | biostudies-literature