Impact of the COVID-19 induced lockdown measures on PM2.5 concentration in USA.
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ABSTRACT:
SUBMITTER: Ghosal R
PROVIDER: S-EPMC8025541 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The recent restrictions on mobility and economic activities imposed by governments due to the COVID-19 pandemic have significantly affected waste production and recycling patterns in cities worldwide. This effect differed both between cities and within cities as the measures of confinement adopted by governments had diverse impacts in different areas of cities, depending on their characteristics (e.g., touristic, or residential). In the present work, mixed waste collection areas were created, based on waste collection points, that define spatial units in which contextual data such as tourism and residential characteristics were aggregated. The difference in mixed waste collected compared with previous years was analyzed along with the impacts on recycling due to the modification in operations regarding waste collection during the lockdown. The results showed that despite the suspension of the door-to-door recycling system during the lockdown, this did not translate into an increase in the production of mixed waste, and the recycling levels of previous years have not been reached after the lockdown, indicating a possible change in recycling habits in Lisbon. The touristic and non-residential mixed waste circuits presented significantly reduced mixed waste production compared to the non-pandemic context. Also, tourist, mobility, and economic activity were measured to understand which factors contributed to waste production changes during the COVID-19 pandemic. While little evidence of a relationship with these exogenous variables was found at the citywide level, evidence was found at the waste collection circuit level.
| S-EPMC8648666 | biostudies-literature
Project description:Stringent lockdown measures implemented in Italy to mitigate the spread of COVID-19 are generating unprecedented economic impacts. However, the environmental consequences associated with the temporary shutdown and recovery of industrial and commercial activities are still not fully understood. Using the well-known carbon footprint (CF) indicator, this paper provides a comprehensive estimation of environmental effects due to the COVID-19 outbreak lockdown measures in Italy. Our aim was to quantify the CF associated with the consumption of energy by any economic activity and region in Italy during the lockdown, and then compare these environmental burdens with the CF calculated for analogous periods from 2015 to 2019 (~March and April). Complementarily, we also conducted a scenario analysis to estimate the post-lockdown CF impact in Italy. A consumption-based approach was applied according to the principles of the established Life Cycle Assessment method. The CF was therefore quantified as a sum of direct and indirect greenhouse gases (GHGs) released from domestically produced and imported energy metabolism flows, excluding the exports. Our findings indicate that the CF in the lockdown period is ~-20% lower than the mean CF calculated for the past. This means avoided GHGs in between ~5.6 and ~10.6 Mt CO2e. Results further suggest that a tendency occurs towards higher impact savings in the Northern regions, on average ~230 kt CO2e of GHGs avoided by province (against ~110-130 kt CO2e in central and Southern provinces). Not surprisingly, these are the utmost industrialized areas of Italy and have been the ones mostly affected by the outbreak. Despite our CF estimates are not free of uncertainties, our research offers quantitative insights to start understanding the magnitude generated by such an exceptional lockdown event in Italy on climate change, and to complement current scientific efforts investigating the relationships between air pollution and the spread of COVID-19.
| S-EPMC7258837 | biostudies-literature
Project description:The lagoon of Venice has always been affected by the regional geomorphological evolution, anthropogenic stressors and global changes. Different morphological settings and variable biogeophysical conditions characterize this continuously evolving system that rapidly responds to the anthropic impacts. When the lockdown measures were enforced in Italy to control the spread of the SARS-CoV-2 infection on March 10th 2020, the ordinary urban water traffic around Venice, one of the major pressures in the lagoon, came to a halt. This provided a unique opportunity to analyse the environmental effects of restrictions to mobility on water transparency. Pseudo true-colour composites Sentinel-2 satellite imagery proved useful for qualitative visual interpretation, showing the reduction of the vessel traffic and their wakes from the periods before and during the SARS-CoV-2 outbreak. A quantitative analysis of suspended matter patterns, based on satellite-derived turbidity, in the absence of traffic perturbations, allowed to focus on natural processes and the residual stress from human activities that continued throughout the lockdown. We conclude that the high water transparency can be considered as a transient condition determined by a combination of natural seasonal factors and the effects of COVID-19 restrictions.
| S-EPMC7242204 | biostudies-literature
Project description:We have investigated the impact of reduced emissions due to COVID-19 lockdown measures in spring 2020 on air quality in Canada's four largest cities: Toronto, Montreal, Vancouver, and Calgary. Observed daily concentrations of NO2, PM2.5, and O3 during a "pre-lockdown" period (15 February-14 March 2020) and a "lockdown" period (22 March-2 May 2020), when lockdown measures were in full force everywhere in Canada, were compared to the same periods in the previous decade (2010-2019). Higher-than-usual seasonal declines in mean daily NO2 were observed for the pre-lockdown to lockdown periods in 2020. For PM2.5, Montreal was the only city with a higher-than-usual seasonal decline, whereas for O3 all four cities remained within the previous decadal range. In order to isolate the impact of lockdown-related emission changes from other factors such as seasonal changes in meteorology and emissions and meteorological variability, two emission scenarios were performed with the GEM-MACH air quality model. The first was a Business-As-Usual (BAU) scenario with baseline emissions and the second was a more realistic simulation with estimated COVID-19 lockdown emissions. NO2 surface concentrations for the COVID-19 emission scenario decreased by 31 to 34% on average relative to the BAU scenario in the four metropolitan areas. Lower decreases ranging from 6 to 17% were predicted for PM2.5. O3 surface concentrations, on the other hand, showed increases up to a maximum of 21% close to city centers versus slight decreases over the suburbs, but Ox (odd oxygen), like NO2 and PM2.5, decreased as expected over these cities.Supplementary informationThe online version contains supplementary material available at 10.1007/s11869-021-01039-1.
| S-EPMC8130219 | biostudies-literature
Project description:Recent studies based on observations have shown the impact of lockdown measures taken in various European countries to contain the Covid-19 pandemic on air quality. However, these studies are often limited to compare situations without and with lockdown measures, which correspond to different time periods and then under different meteorological conditions. We propose a modelling study with the WRF-CHIMERE modelling suite for March 2020, an approach allowing to compare atmospheric composition with and without lockdown measures without the biases of meteorological conditions. This study shows that the lockdown effect on atmospheric composition, in particular through massive traffic reductions, has been important for several short-lived atmospheric trace species, with a large reduction in NO2 concentrations, a lower reduction in Particulate Matter (PM) concentrations and a mitigated effect on ozone concentrations due to non-linear chemical effects.
| S-EPMC7309726 | biostudies-literature
Project description:The impacts of the meteorological condition and emissions reduction on the aerosol concentration over the Beijing–Tianjin–Hebei (BTH) region during the COVID-19 lockdown were analyzed by conducting three numerical experiments, including one with the meteorological field in 2019 and MEIC-2019 (2019 monthly Multi-resolution Emissions Inventory for China), one with the meteorological field in 2020 and MEIC-2019, and one with the meteorological field in 2020 and MEIC-2020, via a WRF-Chem model. The numerical experiments were performed from 3 to 16 February in 2019 and in 2020, during which a severe fog–haze event (3–16 February 2020) occurred in the BTH region, with a simulated maximum daily PM2.5 of 245 μg m−3 in Tangshan and 175 μg m−3 in Beijing. The results indicate that the daily PM2.5 decreased by 5–150 μg m−3 due to the emissions reduction and increased by 10–175 μg m−3 due to the meteorological condition in Beijing, Shijiazhuang, Cangzhou, Handan, Hengshui, Chengde, Zhangjiakou, and Tangshan from 7 to 14 February. For the horizontal distribution, PM2.5 and different aerosol species concentrations from 7 to 14 February 2020 increased compared with those during the same period in 2019, indicating that the accumulation of pollutants caused by the unfavorable meteorological condition offset the decreases caused by the emissions reduction, leading to the high aerosol concentration during the COVID-19 lockdown. 摘要 本研究从气象条件和减排两方面探讨了COVID-19封锁期间严重雾霾发生的原因[研究目的], 并定量的分析了气象条件和减排对PM2.5浓度的相对影响[创新点].使用WRF-Chem在2019年2月3日–16日和2020年2月3日–16日进行了三个数值模拟试验, 分别为2019年气象场与MEIC-2019 (2019年中国多分辨率月度排放清单) ,2020年气象场与MEIC-2019,2020年气象场与MEIC-2020[研究方法].结果表明COVID-19封锁期间严重雾霾的原因是不利气象条件增加的PM2.5浓度大于减排减少的PM2.5浓度[重要结论]. Graphical abstract Image, graphical abstract
| S-EPMC7734416 | biostudies-literature
Project description:ObjectivesAbrupt changes in air pollution levels associated with the coronavirus disease 2019 (COVID-19) outbreak present a unique opportunity to evaluate the effects of air pollution on influenza risk, at a time when emission sources were less active and personal hygiene practices were more rigorous.MethodsThis time-series study examined the relationship between influenza cases (n=22 874) and air pollutant concentrations from 2018 to 2021, comparing the timeframes before and during the COVID-19 pandemic in and around Thailand's Khon Kaen province. Poisson generalized additive modeling was employed to estimate the relative risk of hospitalization for influenza associated with air pollutant levels.ResultsBefore the COVID-19 outbreak, both the average daily number of influenza hospitalizations and particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) concentration exceeded those later observed during the pandemic (p<0.001). In single-pollutant models, a 10 μg/m3 increase in PM2.5 before COVID-19 was significantly associated with increased influenza risk upon exposure to cumulative-day lags, specifically lags 0-5 and 0-6 (p<0.01). After adjustment for co-pollutants, PM2.5 demonstrated the strongest effects at lags 0 and 4, with elevated risk found across all cumulative-day lags (0-1, 0-2, 0-3, 0-4, 0-5, and 0-6) and significantly greater risk in the winter and summer at lag 0-5 (p<0.01). However, the PM2.5 level was not significantly associated with influenza risk during the COVID-19 outbreak.ConclusionsLockdown measures implemented during the COVID-19 pandemic could mitigate the risk of PM2.5-induced influenza. Effective regulatory actions in the context of COVID-19 may decrease PM2.5 emissions and improve hygiene practices, thereby reducing influenza hospitalizations.
| S-EPMC10999304 | biostudies-literature
Project description:The spread of coronavirus (COVID-19) caused an unprecedented implementation of lockdown measures across world's nations. Veneto Region, located in North-Eastern Adriatic Sea was one of the first maritime regions in Italy and Europe subjected to progressive lockdown restrictions. We systematically analyse the effects of national lockdown policies on maritime settings of the region using Automated Identification System (AIS) data from fishing vessels, passenger ships, tanker and cargo vessels collected through the Aqua Alta Oceanographic Tower (AAOT). We derive consequences on vessel activities during the March-April 2020 lockdown, by using a data-driven, comparative spatio-temporal analysis of vessel trajectories. Results show that compared to the same period of 2017, vessel activity were reduced by 69% during the lockdown, fishing activities reduced by 84% and passenger traffic by 78%. We register a restart of fishing activity in the third week of April 2020. We suggest that the presented conceptual and spatial assessment protocol can guide future research on environmental and socio-economic effects of COVID-19 on marine realms and contribute to further interdisciplinary research with other marine scientific fields.
| S-EPMC7286833 | biostudies-literature
Project description:ObjectivesTo assess the potential impacts of successive lockdown-easing measures in England, at a point in the COVID-19 pandemic when community transmission levels were relatively high.DesignWe developed a Bayesian model to infer incident cases and reproduction number (R) in England, from incident death data. We then used this to forecast excess cases and deaths in multiple plausible scenarios in which R increases at one or more time points.SettingEngland.ParticipantsPublicly available national incident death data for COVID-19 were examined.Primary outcomeExcess cumulative cases and deaths forecast at 90 days, in simulated scenarios of plausible increases in R after successive easing of lockdown in England, compared with a baseline scenario where R remained constant.ResultsOur model inferred an R of 0.75 on 13 May when England first started easing lockdown. In the most conservative scenario modelled where R increased to 0.80 as lockdown was eased further on 1 June and then remained constant, the model predicted an excess 257 (95% CI 108 to 492) deaths and 26 447 (95% CI 11 105 to 50 549) cumulative cases over 90 days. In the scenario with maximal increases in R (but staying ≤1), the model predicts 3174 (95% CI 1334 to 6060) excess cumulative deaths and 421 310 (95% CI 177 012 to 804 811) cases. Observed data from the forecasting period aligned most closely to the scenario in which R increased to 0.85 on 1 June, and 0.9 on 4 July.ConclusionsWhen levels of transmission are high, even small changes in R with easing of lockdown can have significant impacts on expected cases and deaths, even if R remains ≤1. This will have a major impact on population health, tracing systems and healthcare services in England. Following an elimination strategy rather than one of maintenance of R ≤1 would substantially mitigate the impact of the COVID-19 epidemic within England.
| S-EPMC8438582 | biostudies-literature
Project description:BackgroundHow effective have lockdowns been at reducing the covid-19 infection and mortality rates? Lockdowns influence contact among persons within or between populations including restricting travel, closing schools, prohibiting public gatherings, requiring workplace closures, all designed to slow the contagion of the virus. The purpose of the present study was to assess the impact of lockdown measures on the spread of covid-19 and test a theoretical model of the covid-19 pandemic employing structural equation modelling.MethodsLockdown variables, population demographics, mortality rates, infection rates, and health were obtained for eight countries: Austria, Belgium, France, Germany, Italy, Netherlands, Spain, and the United Kingdom. The dataset, owid-covid-data.csv, was downloaded on 06/01/2020 from: https://github.com/owid/covid-19-data/tree/master/public/data. Infection spread and mortality data were depicted as logistic growth and analyzed with stepwise multiple regression. The overall structure of the covid-19 data was explored through factor analyses leading to a theoretical model that was tested using latent variable path analysis.ResultsMultiple regression indicated that the time from lockdown had a small but significant effect (β = 0.112, p< 0.01) on reducing the number of cases per million. The stringency index produced the most important effect for mortality and infection rates (β = 0.588,β = 0.702, β = 0.518, β = 0.681; p< 0.01). Exploratory and confirmatory analyses resulted in meaningful and cohesive latent variables: 1) Mortality, 2) Infection Spread, 3) Pop Health Risk, and 4) Health Vulnerability (Comparative Fit Index = 0.91; Standardized Root Mean Square Residual = 0.08).DiscussionThe stringency index had a large impact on the growth of covid-19 infection and mortality rates as did percentage of population aged over 65, median age, per capita GDP, diabetes prevalence, cardiovascular death rates, and ICU hospital beds per 100K. The overall Latent Variable Path Analysis is theoretically meaningful and coherent with acceptable fit indices as a model of the covid-19 pandemic.
| S-EPMC8491873 | biostudies-literature