Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects.
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ABSTRACT:
SUBMITTER: Han Y
PROVIDER: S-EPMC8563087 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:As a result of the lockdown (LD) control measures enacted to curtail the COVID-19 pandemic in Wuhan, almost all non-essential human activities were halted beginning on January 23, 2020 when the total lockdown was implemented. In this study, changes in the concentrations of the six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Wuhan were investigated before (January 1 to 23, 2020), during (January 24 to April 5, 2020), and after the COVID-19 lockdown (April 6 to June 20, 2020) periods. Also, the relationships between the air pollutants and meteorological variables during the three periods were investigated. The results showed that there was significant improvement in air quality during the lockdown. Compared to the pre-lockdown period, the concentrations of NO2, PM2.5, PM10, and CO decreased by 50.6, 41.2, 33.1, and 16.6%, respectively, while O3 increased by 149% during the lockdown. After the lockdown, the concentrations of PM2.5, CO and SO2 declined by an additional 19.6, 15.6, and 2.1%, respectively. However, NO2, O3, and PM10 increased by 55.5, 25.3, and 5.9%, respectively, compared to the lockdown period. Except for CO and SO2, WS had negative correlations with the other pollutants during the three periods. RH was inversely related with all pollutants. Positive correlations were observed between temperature and the pollutants during the lockdown. Easterly winds were associated with peak PM2.5 concentrations prior to the lockdown. The highest PM2.5 concentrations were associated with southwesterly wind during the lockdown, and northwesterly winds coincided with the peak PM2.5 concentrations after the lockdown. Although, COVID-19 pandemic had numerous negative effects on human health and the global economy, the reductions in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits. This study improves the understanding of the mechanisms that lead to air pollution under diverse meteorological conditions and suggest effective ways of reducing air pollution in Wuhan.
| S-EPMC7657938 | biostudies-literature
Project description:BackgroundMeteorological factors and air pollutants have been reported to be associated with hand, foot, and mouth disease (HFMD) epidemics before the introduction of vaccine. However, there is limited evidence for studies with long-term dimensions.MethodsWe collected the daily HFMD counts, weather and air pollution data from 2014 to 2020 in Chengdu. Distributed lag non-linear models (DLNM) were used to assess the associations of meteorological factors and air pollutants on HFMD cases.ResultsFrom 2014-2020, high relative humidity and precipitation and extremely high and low levels of PM10, O3, SO2 and CO increased the risk of HFMD. In pre-vaccination period, extreme high and low temperatures, PM10 and NO2, low precipitation and high concentrations of PM2.5 and O3 significantly increase the risk of HFMD; In post-vaccination period, high relative humidity and low level of CO can significantly increase the incidence of HFMD; During the period of COVID-19, only low temperature will significantly increase the risk of HFMD; Low concentration of air pollutants has the greatest impact on the 6-14 age group, while the high concentration of air pollutants has the greatest impact on the 0-1 age group.ConclusionsOur study suggest that high relative humidity and precipitation and extremely high and low levels of PM10, O3, SO2 and CO increased the risk of HFMD from 2014 to 2020. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.
| S-EPMC9112249 | biostudies-literature
Project description:BackgroundPreterm birth is the leading cause of perinatal morbidity and mortality in the U.S. and disparities among racial and ethnic groups persist. While etiologies of preterm birth have not been fully elucidated, it is probable that environmental and social factors play a role.ObjectiveWe hypothesized that there is an interactive association between exposure to fine particulate matter (PM2.5) or ozone (O3) and neighborhood socioeconomic factors that increase the risk of preterm birth.MethodsWe conducted a retrospective study using geocoded birth certificate data between 2007 and 2011, daily ambient air quality data on PM2.5 and O3, and American Community Survey (2007-2011 5-year estimates) data to assess census tract-level socioeconomic factors in California urban counties.ResultsOur study found a small positive association between maternal exposures to PM2.5 and O3 and preterm birth that varied by gestational exposure period. In mixed-effects models, we found an increase in the risk of preterm birth for a one-unit change in PM2.5 averaged across the entire pregnancy (AOR = 1.02, 95% CI: 1.01, 1.02) and O3 during 3-months pre-pregnancy (AOR = 1.03, 95% CI: 1.02, 1.04). Interaction between census tract-level factors and air pollutants showed an increase in the risk of preterm birth among mothers living in higher socioeconomic areas, though, a fixed cohort bias sensitivity analysis showed these associations were not significant.SignificanceThese findings substantiate previous studies that showed associations between air pollution and preterm birth, even as pollution levels have decreased. This study has important implications for policy decisions and may help inform research on potential mechanisms of preterm birth.
| S-EPMC8134052 | biostudies-literature
Project description:Various countries across the globe have been affected by different COVID-19 waves at different points in time and with varying levels of virulence. With the backdrop of the two COVID-19 waves that broke out in Delhi, this study examines the variations in the concentrations of criteria pollutants, air quality, and meteorological variables across the waves and their influence on COVID-19 morbidity/mortality. Descriptive statistics, violin plots, and Spearman rank correlation tests were employed to assess the variations in environmental parameters and investigate their associations with COVID-19 incidence under the two waves. The susceptible-infected-recovered model and multiple linear regression were used to assess the wave-wise basic reproduction number (R0) and infection spreading trajectory of the virus. Our results show that the first wave in Delhi had three successive peaks and valleys, and the first peak of the second wave was the tallest, indicating the severity of per-day infection cases. During the analysed period (April 2020 and April 2021), concentrations of criteria pollutants varied across the waves, and air pollution was substantially higher during the second wave. In addition, the results revealed that during the second wave, NO2 maintained a significant negative relationship with COVID-19 (cases per day), while SO2 had a negative relationship with COVID-19 (cumulative cases) during the first wave. Our results also show a significant positive association of O3 with COVID-19 deaths during the first wave and cumulative cases and deaths during the second wave. The study indicates that a higher relative humidity in Delhi had a negative relation with COVID-19 cumulative cases and mortality during the first wave. The study confirms that the estimated R0 was marginally different during the two waves, and the spread of COVID-19 new cases followed a cubic growth trajectory. The findings of this study provide valuable information for policymakers in handling COVID-19 waves in various cities.
| S-EPMC8610833 | biostudies-literature
Project description:Lombardy, the most populated and industrialized Italian region, was the epicentre of the first wave (March and April 2020) of COVID-19 in Italy and it is among the most air polluted areas of Europe. We carried out an ecological study to assess the association between long-term exposure to particulate matter (PM) and nitrogen dioxide (NO2) on COVID-19 incidence and all-cause mortality after accounting for demographic, socioeconomic and meteorological variables. The study was based on publicly available data. Multivariable negative binomial mixed regression models were fitted, and results were reported in terms of incidence rate ratios (IRRs) and standardized mortality ratios (SMR). The effect of winter temperature and humidity was modelled through restricted cubic spline. Data from 1439 municipalities out of 1507 (95%) were included in the analyses, leading to a total of 61,377 COVID-19 cases and 40,401 deaths from all-causes collected from February 20th to April 16th and from March 1st to April 30th, 2020, respectively. Several demographic and socioeconomic variables resulted significantly associated with COVID-19 incidence and all-cause mortality in a multivariable fashion. An increase in average winter temperature was associated with a nonlinear decrease in COVID-19 incidence and all-cause mortality, while an opposite trend emerged for the absolute humidity. An increase of 10 μg/m3 in the mean annual concentrations of PM2.5 and PM10 over the previous years was associated with a 58% and 34% increase in COVID-19 incidence rate, respectively. Similarly, a 10 μg/m3 increase of annual mean PM2.5 concentration was associated with a 23% increase in all-cause mortality. An inverse association was found between NO2 levels and COVID-19 incidence and all-cause mortality. Our ecological study showed that exposure to PM was significantly associated with the COVID-19 incidence and excess mortality during the first wave of the outbreak in Lombardy, Italy.
| S-EPMC7826113 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R0) was calculated with the maximum likelihood "removal" method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R0 was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R0 and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R0, while nitrogen dioxide (NO2), 24-h average concentration of nitrogen dioxide (NO2_24 h) and daily maximum temperature were inversely correlated to R0, with coefficients of 0.644, 0.661, -0.636, -0.657, -0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R0, including mean/maximum/minimum air pressure and mean wind speed, with coefficients of -0.697, -0.697, -0.697 and -0.841, respectively. There was no correlation with R0 and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.
| S-EPMC7406240 | biostudies-literature
Project description:ObjectiveTo determine the association of meteorological factors and air pollutants (MFAPs) with fracture and to estimate the effect size/time lag.DesignThis is a nationwide population-based ecological study from 2008 to 2017.SettingEight large metropolitan areas in Korea.ParticipantsOf 8 093 820 patients with fractures reported in the Korea National Health Insurance database, 2 129 955 were analysed after the data set containing patient data (age, sex and site of fractures) were merged with MFAPs. Data on meteorological factors were obtained from the National Climate Data Center of the Korea Meteorological Administration. Additionally, data on air pollutants (atmospheric particulate matter ≤2.5 µm in diameter (PM2.5), PM10, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide) were obtained from the Air Korea database.Primary and secondary outcome measuresWe hypothesised that there would be an association between MFAPs and the incidence of fracture. A generalised additive model was used while factoring in the non-linear relationship between MFAPs and fractures as well as a time lag ≤7 days. Multivariate analysis was performed. Backward elimination with an Akaike information criterion was used to fit the multivariate model.ResultsOverall, in eight urban areas, 2 129 955 patients with fractures were finally analysed. These included 370 344, 187 370, 173 100, 140 358, 246 775, 6501, 228 346, 57 183 and 719 978 patients with hip, knee, shoulder, elbow, wrist, hand, ankle, foot and spine fractures, respectively. Various MFAPs (average temperature, daily rain, wind speed, daily snow and PM2.5) showed significant association with fractures, with positive correlations at time lags 7, 5-7, 5-7, 3-7 and 6-7 days, respectively.ConclusionsVarious MFAPs could affect the occurrence of fractures. The average temperature, daily rain, wind speed, daily snow and PM2.5 were most closely associated with fracture. Thus, improved public awareness on these MFAPs is required for clinical prevention and management of fractures.
| S-EPMC8202114 | biostudies-literature
Project description:Steady meteorological conditions are important external factors affecting air pollution. In order to analyze how adverse meteorological variables affect air pollution, surface synoptic situation patterns and meteorological conditions during heavy pollution episodes are discussed. The results showed that there were 78 RPHPDs (regional PM2.5 pollution days) in Jiangsu, with a decreasing trend year by year. Winter had the most stable meteorological conditions, thus most RPHPDs appeared in winter, followed by autumn and summer, with the least days in spring. RPHPDs were classified into three patterns, respectively, as equalized pressure (EQP), advancing edge of a cold front (ACF) and inverted trough of low pressure (INT) according to the SLP (sea level pressure). RPHPDs under EQP were the most (51%), followed by ACF (37%); INT was the minimum (12%). Using statistical methods and meteorological condition data on RPHPDs from 2013 to 2017 to deduce the thresholds and 2018 as an independent dataset to validate the proposed thresholds, the threshold values of meteorological elements are summarized as follows. The probability of RPHPDs without rain was above 92% with the daily and hourly precipitation of all RPHPDs below 2.1 mm and 0.8 mm. Wind speed, RHs, inversion intensity(ITI), height difference in the temperature inversion(ITK), the lower height of temperature inversion (LHTI) and mixed-layer height (MLH) in terms of 25%-75% high probability range were respectively within 0.5-3.6 m s-1, 55%-92%, 0.7-4.0 °C 100 m -1, 42-576 m, 3-570 m, 200-1200 m. Two conditions should be considered: whether the pattern was EQP, ACF or INT and whether the eight meteorological elements are within the thresholds. If both criteria are met, PM2.5 particles tend to accumulate and air pollution diffusion conditions are poor. Unfavorable meteorological conditions are the necessary, but not sufficient condition for RPHPDs.
| S-EPMC7177968 | biostudies-literature
Project description:The aim of this research is to study the influence of atmospheric pollutants and meteorological variables on the incidence rate of COVID-19 and the rate of hospital admissions due to COVID-19 during the first and second waves in nine Spanish provinces. Numerous studies analyze the effect of environmental and pollution variables separately, but few that include them in the same analysis together, and even fewer that compare their effects between the first and second waves of the virus. This study was conducted in nine of 52 Spanish provinces, using generalized linear models with Poisson link between levels of PM10, NO2 and O3 (independent variables) and maximum temperature and absolute humidity and the rates of incidence and hospital admissions of COVID-19 (dependent variables), establishing a series of significant lags. Using the estimators obtained from the significant multivariate models, the relative risks associated with these variables were calculated for increases of 10 µg/m3 for pollutants, 1 °C for temperature and 1 g/m3 for humidity. The results suggest that NO2 has a greater association than the other air pollution variables and the meteorological variables. There was a greater association with O3 in the first wave and with NO2 in the second. Pollutants showed a homogeneous distribution across the country. We conclude that, compared to other air pollutants and meteorological variables, NO2 is a protagonist that may modulate the incidence and severity of COVID-19, though preventive public health measures such as masking and hand washing are still very important.Supplementary informationThe online version contains supplementary material available at 10.1007/s13762-022-04190-z.
| S-EPMC9065237 | biostudies-literature
Project description:BackgroundTransmission intensity for mosquito-borne diseases are highly heterogenous and multi-factorial. Understanding risk factors associated to disease transmission allow the optimization of vector control. This study sets out to understand and compare the combined anthropogenic and environmental risk factors of four major mosquito-borne diseases, dengue, malaria, chikungunya and Japanese encephalitis in Thailand.MethodsAn integrated analysis of mosquito-borne diseases, meteorological and ambient air pollutants of 76 provinces of Thailand was conducted over 2003-2021. We explored the use of generalized linear models and generalized additive models to consider both linear and non-linear associations between meteorological factors, ambient air pollutants and mosquito-borne disease incidence. Different assumptions on spatio-temporal dependence and nonlinearity were considered through province-specific and panel models, as well as different spline functions. Disease-specific model evidence was assessed to select best-fit models for epidemiological inference downstream.ResultsAnalyses indicated several findings which can be generally applied to all diseases explored: (1) higher AH above mean values was positively associated with disease case counts (2) higher total precipitation above mean values was positively associated with disease case counts (3) extremely high temperatures were negatively associated with disease case counts (4) higher SO2 and PM2.5 surface concentrations were negatively associated with disease case counts. However, the relationships between disease and RH, non-extreme temperatures and CO surface concentration were more mixed, with directions of associations changing across the different diseases considered.ConclusionsThis study found protective and enhancing effects of meteorological and ambient air pollutant factors on mosquito-borne diseases burdens in Thailand. Further studies should employ these factors to understand and predict risk factors associated with mosquito-borne disease transmission.
| S-EPMC10752508 | biostudies-literature