Modelling the effect of local and regional emissions on PM2.5 concentrations in Wuhan, China during the COVID-19 lockdown
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ABSTRACT:
SUBMITTER: BAI Y
PROVIDER: S-EPMC8524808 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWuhan was the epicenter of the Coronavirus Disease 2019 (COVID-19), outbreak in China. We aimed at surveying the general public in China to better understand their levels of psychological state and its influencing factors after the Wuhan shutdown on January 23.MethodsThe cross-sectional survey was conducted online and 4,701 respondents participated in this survey. Of them, 3,803 respondents were considered for final analysis. Data on subjective indicators of daily life changes were collected, and individual scores on changes in anxiety, depression, and stress were tested by 8-item, 11-item, and 6-item questionnaires, respectively.ResultsMultivariable regression analyses showed that living in the rural areas, living in the other regions except Hubei, and higher education were independent correlates of less negative emotions. Besides, the level of attention, self-assessed infection risk, impact on the daily life and mental health help-seeking tended to be positively associated with the scores of anxiety, depression, and stress.ConclusionsCity of residence, education, marital status, monthly income, level of attention, self-assessed infection risk, impact on daily life and mental health help-seeking were important correlates of anxiety, depression, and stress scores.
| S-EPMC10081786 | biostudies-literature
Project description:PM2.5 concentrations throughout much of the U.S. have decreased over the last 15 years, but emissions and concentration trends can vary by location and source type. Such trends should be understood to inform air quality management and policies. This work examines trends in emissions, concentrations and source apportionments in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. Annual and seasonal trends were investigated using National Emission Inventory (NEI) data for 2002 to 2011, speciated ambient PM2.5 data from 2001 to 2014, apportionments from positive matrix factorization (PMF) receptor modeling, and quantile regression. Over the study period, county-wide data suggest emissions from point sources decreased (Detroit) or held constant (Chicago), while emissions from on-road mobile sources were constant (Detroit) or increased (Chicago), however changes in methodology limit the interpretation of inventory trends. Ambient concentration data also suggest source and apportionment trends, e.g., annual median concentrations of PM2.5 in the two cities declined by 3.2 to 3.6 %/yr (faster than national trends), and sulfate concentrations (due to coal-fired facilities and other point source emissions) declined even faster; in contrast, organic and elemental carbon (tracers of gasoline and diesel vehicle exhaust) declined more slowly or held constant. The PMF models identified nine sources in Detroit and eight in Chicago, the most important being secondary sulfate, secondary nitrate and vehicle emissions. A minor crustal dust source, metals sources, and a biomass source also were present in both cities. These apportionments showed that the median relative contributions from secondary sulfate sources decreased by 4.2 to 5.5% per year in Detroit and Chicago, while contributions from metals sources, biomass sources, and vehicles increased from 1.3 to 9.2% per year. This first application of quantile regression to trend analyses of speciated PM2.5 data reveals that source contributions to PM2.5 varied as PM2.5 concentrations decreased, and that the fraction of PM2.5 due to emissions from vehicles and other local emissions has increased. Each data source has uncertainties, but emissions, monitoring and PMF data provide complementary information that can help to discern trends and identify contributing sources. Study results emphasize the need to target specific sources in policies and regulations aimed at decreasing PM2.5 concentrations in urban areas.
| S-EPMC5603263 | biostudies-literature
Project description:Specification of PM2.5 spatial and temporal characteristics is important for understanding PM2.5 adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM2.5 emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The results showed that for PM2.5 emissions from industrial sector 8 clusters were found in 2008 but they merged together into a huge cluster in 2010, suggesting that industrial sector underwent an integrating process. For PM2.5 emissions from electricity generation sector, strong locality of clusters was revealed, implying that each region had its own electricity generation system. For PM2.5 emissions from residential sector, the same pattern of 10 clusters was uncovered in both years, implicating the household energy consumption unchanged from 2008 to 2010. For PM2.5 emissions from transportation sector, the same pattern of 5 clusters with many connections in-between was unraveled, indicating the high-speed development of transportation nationalwidely. Except for the known elements, mercury (Hg) surfaced as an element for particle nucleation. To our knowledge, this is the first network study in this field.
| S-EPMC5016853 | biostudies-other
Project description:Carbon emissions trading is a market-based tool for solving environmental issues. This study used a difference-in-differences (DID) approach to estimate China's carbon trading pilots to reduce PM2.5 concentrations. The results of this quasi-natural experiment show that the carbon trading policy effectively reduces PM2.5 by 2.7 μg/m3. We used a propensity score matching (PSM-DID) method to minimize selection bias to construct a treatment and a control group. The results show the policy effect is robust, with a PM2.5 concentration reduction of 2.6 μg/m3. Furthermore, we employed a series of robustness checks to support our findings, which notably indicate that the effect of carbon trading on reducing PM2.5 differs across regions over the years. The western region of China tends to be the most easily affected region, and the early years of carbon trading show slightly greater reduction effects. Our findings provide valuable policy implications for establishing and promoting carbon trading in China and other countries.
| S-EPMC9738372 | biostudies-literature
Project description:PM2.5 pollution has gradually attracted people's attention due to its important negative impact on public health in recent years. The influence of anthropogenic emission factors on PM2.5 concentrations is more complicated, but their relative individual impact on different emission sectors remains unclear. With the aid of the geographic detector model (GeoDetector), this study evaluated the impacts of anthropogenic emissions from different sectors on the PM2.5 concentrations of major cities in China. The results indicated that the influence of anthropogenic emissions factors with different emission sectors on PM2.5 concentrations exhibited significant changes at different spatial and temporal scales. Residential emissions were the dominant driver at the national annual scale, and the NOX of residential emissions explained 20% (q = 0.2) of the PM2.5 concentrations. In addition, residential emissions played the leading role at the regional annual scale and during most of the seasons in northern China, and ammonia emissions from residents were the dominant factor. Traffic emissions play a leading role in the four seasons for MUYR and EC in southern China, MYR and NC in northern China, and on a national scale. Compared with primary particulate matter, secondary anthropogenic precursors have a more important effect on PM2.5 concentrations at the national or regional annual scale. The results can help to strengthen our understanding of PM2.5 pollution, improve PM2.5 forecasting models, and formulate more precise government control policy.
| S-EPMC8535752 | biostudies-literature
Project description:Indoor fine particulate matter (PM2.5) is important since people spend most of their time indoors. However, knowledge of the spatiotemporal variations of indoor PM2.5 concentrations within a city is limited. In this study, the spatiotemporal distributions of indoor PM2.5 levels in Nanjing, China were modeled by the multizone airflow and contaminant transport program (CONTAM), based on the geographically distributed residences, human activities, and outdoor PM2.5 concentrations. The accuracy of the CONTAM model was verified, with a good agreement between the model simulations and measurements (r = 0.940, N = 110). Two different scenarios were considered to examine the building performance and influence of occupant behaviors. Higher PM2.5 concentrations were observed under the scenario when indoor activities were considered. Seasonal variability was observed in indoor PM2.5 levels, with the highest concentrations occurring in the winter and the lowest occurring in the summer. Building characteristics have a significant effect on the spatial distribution of indoor PM2.5 concentrations, with multistory residences being more vulnerable to outdoor PM2.5 infiltration than high-rise residences. The overall population exposure to PM2.5 in Nanjing was estimated. It would be overestimated by 16.67% if indoor exposure was not taken into account, which would lead to a bias in the health impacts assessment.
| S-EPMC6339030 | biostudies-literature
Project description:Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ? 2.5 ?m) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data.We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.The overall model cross-validation R(2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R(2) = 0.73, regression slope = 0.91) and seasonal (R(2) = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 ?g/m(3) between 2004 and 2007 and a decrease of 0.46 ?g/m(3) between 2008 and 2013.Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.
| S-EPMC4749081 | biostudies-other
Project description:Fine (PM(2.2)) and coarse (PM(2.2-10)) particles concurrently collected in urban (Hanoi) and rural (Lucnam) areas were analyzed for ionic and elemental compositions to provide input for PMF receptor modeling of emission sources in the Red River Delta (RRD), a key economic development region in Vietnam. Long-range transport (LRT) aerosol, coal fly ash from major coal-fired plants in RRD, and marine aerosols are regional sources, which explain the minor variability of the mass concentrations of fine particles across the region. Local sources include soil/resuspended road dust, local coal fly ash, and biomass burning. Soil/resuspended road dust is the largest source component of coarse particles at the two sites. It is more abundant in Hanoi than in Lucnam reflecting the urban-rural contrast in traffic and construction works. Receptor models reveal the incorporation of secondary sulfate, nitrate, and ammonium into the various primary particles, i.e., soot, minerals, soil organic matter, and sea salt. Soot particles from LRT carry the largest amounts of sulfate and ammonium mass concentrations measured at the two sites. Based on receptor models, the yields and possible chemical forms of secondary sulfate, nitrate, and ammonium in different types of primary particles can be inferred.
| S-EPMC2770131 | biostudies-other
Project description:On 8 April 2020, the Chinese government lifted the lockdown and opened up public transportation in Wuhan, China, the epicentre of the COVID-19 pandemic. After 76 days in lockdown, Wuhan residents were allowed to travel outside of the city and go back to work. Yet, given that there is still no vaccine for the virus, this leaves many doubting whether life will indeed go back to normal. The aim of this research was to track longitudinal changes in motivation for self-isolating, life-structured, indicators of well-being and mental health after lockdown was lifted. We have recruited 462 participants in Wuhan, China, prior to lockdown lift between 3 and 7 April 2020 (Time 1), and have followed up with 292 returning participants between 18 and 22 April 2020 (Time 2), 284 between 6 and 10 May 2020 (Time 3), and 279 between 25 and 29 May 2020 (Time 4). This four-wave study used latent growth models to examine how Wuhan residents' psychological experiences change (if at all) within the first two months after lockdown was lifted. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 2 June 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the OSF at https://osf.io/g2t3b. This preregistration was performed prior to data analysis. Generally, our study found that: (i) a majority of people still continue to value self-isolation after lockdown was lifted; (ii) by the end of lockdown, people perceived gradual return to normality and restored structure of everyday life; (iii) the psychological well-being slightly improved after lockdown was lifted; (iv) people who used problem solving and help-seeking as coping strategies during lockdown had better well-being and mental health by the end of the lockdown; (v) those who experienced more disruptions in daily life during lockdown would display more indicators of psychological ill-being by the end of the lockdown.
| S-EPMC7540789 | biostudies-literature
Project description:Wuhan was the first city to adopt the lockdown measures to prevent COVID-19 spreading, which improved the air quality accordingly. This study investigated the variations in chemical compositions, source contributions, and regional transport of fine particles (PM2.5) during January 23-February 22 of 2020, compared with the same period in 2019. The average mass concentration of PM2.5 decreased from 72.9 ?g m-3 (2019) to 45.9 ?g m-3 (2020), by 27.0 ?g m-3. It was predominantly contributed by the emission reduction (92.0%), retrieved from a random forest tree approach. The main chemical species of PM2.5 all decreased with the reductions ranging from 0.85 ?g m-3 (chloride) to 9.86 ?g m-3 (nitrate) (p < 0.01). Positive matrix factorization model indicated that the mass contributions of seven PM2.5 sources all decreased. However, their contribution percentages varied from -11.0% (industrial processes) to 8.70% (secondary inorganic aerosol). Source contributions of PM2.5 transported from potential geographical regions showed reductions with mean values ranging from 0.22 to 4.36 ?g m-3. However, increased contributions of firework burning, secondary inorganic aerosol, road dust, and vehicle emissions from transboundary transport were observed. This study highlighted the complex and nonlinear response of chemical compositions and sources of PM2.5 to air pollution control measures, suggesting the importance of regional-joint control.
| S-EPMC7274103 | biostudies-literature