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Mosquito borne diseases are increasingly problematic as climate change continues to alter patterns of precipitation, flooding, and temperatures that may favor mosquito habitats. Stormwater control measures (SCMs), ecologically sustainable methods of stormwater management, may have varying impacts on Culex mosquitoes, such as in areas with combined sewer overflows (CSOs). We studied spatial and temporal associations of SCMs and Culex mosquito counts surrounding the SCMs, stratifying our examination amongst those that do/do not use pooling and/or vegetation, as well as surrounding CSO outfalls after heavy rainfall (≥95th percentile) during summer 2018. Results indicate Culex mosquito counts after heavy rainfall were not significantly different at SCMs that use vegetation and/or ponding from at those that do not. We also found a 35.5% reduction in the increase of Culex mosquitoes the day of, and 77.0% reduction 7–8 days after, heavy rainfall at CSO outfalls treated with medium SCM density compared to those without SCMs. Our results suggest that SCMs may be associated with a reduction in the increase of Culex mosquitoes at the CSO outfalls after heavy rainfall. More research is needed to study how the impacts of SCMs on mosquito populations may affect human health.

Studies have suggested links between ambient air pollution and coronavirus 2019 (COVID-19) mortality, yet confirmation by well-designed epidemiological studies with individual data is needed.We aimed to examine whether short-term exposure to air pollution is associated with risk of mortality from COVID-19 for those infected with COVID-19.The Cook County Medical Examiner's Office reports individual-level data for deaths from COVID-19 that occur in its jurisdiction, which includes all confirmed COVID-19 deaths in Cook County, Illinois. Case-crossover analysis was conducted to estimate the associations of estimated short-term exposures to particulate matter (PM) with aerodynamic diameterOf the 7,462 case and 25,457 self-control days, almost all were days with exposure levels below theShort-term exposure to air pollution below the NAAQS may increase the mortality burden from COVID-19. https://doi.org/10.1289/EHP10836.

Combined sewer systems (CSS) are water management systems that collect and transport stormwater and sewer water in the same pipes. During large storm events, stormwater runoff may exceed the capacity of the system and lead to combined sewer overflows (CSOs), where untreated sewer and stormwater are released into the environment. Though current literature reveals inconclusive evidence regarding the infectivity of SARS-CoV-2 in wastewater, detection of infectious SARS-CoV-2 in urine and feces of COVID-19 patients led to concerns that areas contaminated by CSOs may be a reservoir of SARS-CoV-2 and may result in illness after the ingestion and/or inhalation of contaminated splashes, droplets, or aerosols. We investigated the association between COVID-19 incidence and CSSs and whether this association differed by precipitation and percent impervious surfaces as a proxy for possible CSOs. We fitted a quasi-Poisson regression model to estimate the change in percentage of incidence rate of COVID-19 cases in counties with a CSS compared to those without, adjusting for potential confounders (i.e., state, population density, date of first documented COVID-19 case, social vulnerability, and percent vaccinated) and including interaction variables between CSS, precipitation, and impervious surfaces. Our findings suggest that heavy precipitation in combination with high percentages of imperviousness is associated with higher incidences of COVID-19 cases in counties with a CSS compared to in counties without (p-value = 2.5e-9). For example, CSS-counties with precipitation of 10 in/month may observe a higher incidence in COVID-19 cases compared to non-CSS counties if their impervious surfaces exceed 33.5% [95%CI: 23.0%, 60.0%]. We theorize that more COVID-19 cases may be seen in counties with a CSS, heavy precipitation, and high percentages of impervious surfaces because of the possible increase in frequency and severity of CSOs. The results suggest links between climate change, urbanization, and COVID-19., Graphical abstract Unlabelled Image

Transnational immigration has increased since the 1950s. In countries such as the United States, immigrants now account forWe summarized current knowledge comparing immigrants' and nonimmigrants' exposure to and health effects of environmental exposures.We conducted a title and abstract review on articles identified through PubMed and selected those that assessed environmental exposures or health effects separately for immigrants and nonimmigrants. After a full text review, we extracted the main findings from eligible studies and categorized each article as exposure-focused, health-focused, or both. We also noted each study's exposure of interest, study location, exposure and statistical methods, immigrant and comparison groups, and the intersecting socioeconomic characteristics controlled for.We conducted a title and abstract review on 3,705 articles, a full text review on 84, and extracted findings from 50 studies. There were 43 studies that investigated exposure (e.g., metals, organic compounds, fine particulate matter, hazardous air pollutants) disparities, but only 12 studies that assessed health disparities (e.g., mortality, select morbidities). Multiple studies reported higher exposures in immigrants compared with nonimmigrants. Among immigrants, studies sometimes observed exposure disparities by country of origin and time since immigration. Of the 50 studies, 43 were conducted in North America.The environmental health of immigrants remains an understudied area, especially outside of North America. Although most identified studies explored potential exposure disparities, few investigated subsequent differences in health effects. Future research should investigate environmental health disparities of immigrants, especially outside North America. Additional research gaps include the role of immigrants' country of origin and time since immigration, as well as the combined effects of immigrant status with intersecting socioeconomic characteristics, such as race/ethnicity, income, and education attainment. https://doi.org/10.1289/EHP9855.

Background Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts. Methods We discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002–2013, and time-series analysis using real data for seven major South Korean cities, 2006–2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods. Results Simulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency. Conclusions Our findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.

Bone health is a major concern for aging populations globally. Osteoporosis and bone mineral density are associated with air pollution, but less is known about the impacts of air pollution on osteoporotic fracture. We aimed to assess the associations between long-term air pollution exposure and risk of osteoporotic fracture in seven large Korean cities. We used Cox proportional hazard models to estimate hazard rations (HRs) of time-varying moving window of past exposures of particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) for osteoporotic fracture in Korean adults (age ≥ 50 years) in the National Health Insurance Service-National Sample Cohort data, followed 2002 to 2015. HRs were calculated for an interquartile range (IQR) increase. Comorbidity and prescription associated with osteoporosis, age, sex, body mass index, health behaviors, and income were adjusted in the models. Effect modification by age, sex, exercise, and income was examined. We assessed 56,467 participants over 535,481 person-years of follow up. Linear and positive exposure-response associations were found for SO2, while PM10 and NO2 showed nonlinear associations. SO2 was associated with osteoporosis-related fracture with marginal significance (HR for an IQR [2 ppb] increase = 1.04, 95% CI: 1.00, 1.09). The SO2 HR estimates were robust in analyses applying various moving windows of exposure (from one to three years of past exposure) and two-pollutant models. The central HR estimate of O3 implied positive associations but was not significant (HR for 0.007 ppm increase = 1.01, 95% CI: 0.97, 1.06). PM10, CO, and NO2 did not show associations. Vulnerable groups by sex, age, exercise, and income varied across air pollutants and there was no evidence of effect modifications. Long-term exposure to SO2, but not PM10, CO, NO2 and O3, was associated with increased osteoporotic fracture risks in Korean adults.

Propensity score (PS) matching to estimate causal effects of exposure is biased when unmeasured spatial confounding exists. Some exposures are continuous yet dependent on a binary variable (e.g., level of a contaminant (continuous) within a specified radius from residence (binary)). Further, unmeasured spatial confounding may vary by spatial patterns for both continuous and binary attributes of exposure. We propose a new generalized propensity score (GPS) matching method for such settings, referred to as conditional GPS (CGPS)-based spatial matching (CGPSsm). A motivating example is to investigate the association between proximity to refineries with high petroleum production and refining (PPR) and stroke prevalence in the southeastern United States. CGPSsm matches exposed observational units (e.g., exposed participants) to unexposed units by their spatial proximity and GPS integrated with spatial information. GPS is estimated by separately estimating PS for the binary status (exposed vs. unexposed) and CGPS on the binary status. CGPSsm maintains the salient benefits of PS matching and spatial analysis: straightforward assessments of covariate balance and adjustment for unmeasured spatial confounding. Simulations showed that CGPSsm can adjust for unmeasured spatial confounding. Using our example, we found positive association between PPR and stroke prevalence. Our R package, CGPSspatialmatch, has been made publicly available., Comment: Online supplementary materials are appended at the bottom of the main pdf

Background Yellow Dust (YD) is a natural source of particulate matter (PM) in Korea. It remarkably increases the concentration of PM. However, characteristics of PM in YD period are different from those of PM in non-YD period. Objectives To investigate whether the association of PM with mortality is different between all days and non-YD days in Seoul, Korea, 1998–2015. Methods We applied time-stratified case-crossover design to estimate effects of PM10 and PM2.5 on non-accidental cardiovascular and respiratory mortality. Effect estimates of PM were compared for all days in the study period and days without YD events. To identify whether different effect estimates between all days and non-YD days were not merely caused by the exclusion of high PM concentrations but rather by YD itself, we estimated effects of PM by randomly excluding the same number of days as days of YD. Results A total of 4,509,392 deaths were observed during the study period. A 10 μg/m3 increase in PM10 or PM2.5 was associated with a 0.15% (95% CI: 0.06% to 0.24%) or 0.27% (95% CI: 0.07% to 0.47%) increase in risk of non-accidental mortality for all days, respectively. These associations were changed to 0.30% (95% CI: 0.18% to 0.42%) and 0.33% (95% CI: 0.10% to 0.55%) when YD days were excluded from analyses. We also found that effect estimates of PM were larger when YD days were excluded than those when high PM concentrations were randomly excluded. Conclusions The effect estimates of PM differed between all days and non-YD days. Our study suggests that including YD days in the analyses is likely to attenuate the effect of PM in a usual urban environment.

Lag is one of the major uncertainties in the heterogeneity of short-term effects of particulate matter with aerodynamic diameter

The choice of lag length is a matter of uncertainty in air pollution time-series studies. Lag models and model selections are widely used for inferences about lag effects, but there is lack of discussion on the integration of the two. We aimed to provide theoretical discussion on the performance of lag models, and the impact of model selections on inferences about lag effects. Bias and model selections based upon information criteria, statistical significance, effect size, and model averaging were discussed in the context of lag analysis. A simulation with eight of PM2.5-mortality relation scenarios was also conducted in order to explore the performances of lag models and to compare the model selections. The application of lag models with an insufficient lag interval taken into account (i.e. insufficient lag models) provides biased estimates. We provided features of the model selections and showed their pitfalls in lag analysis of air pollution time-series studies. We also discussed limitations of meta-analysis which fails to consider the application of different lag models in individual studies. To foster exploration on air pollution-lag-response relations with relevant tools, we encourage researchers to compare different lag models in terms of effect estimates and variance estimates, and to report their favored models and competing models together based upon scientific knowledge supporting lag-response relations.

The objective of this study was to identify the association between social deprivation, outdoor air pollution, and tuberculosis (TB) incidence rate or mortality rate. The study sample comprised 25 districts in Seoul, Korea. We used two public data derived from the Community Health Survey and Seoul Statistics. The geographic information system analysis and random effects Poisson regression were applied to explore the association of social deprivation and air pollution with TB incidence and mortality. An 1 ppb increase in sulfur dioxide (SO

Health effects of PM10 on mortality have been studies worldwide. However, not many studies have questioned possibility that short-term effects of PM10 can be changed over time. Identifying temporal variability of the effects would be of help to getting more valid PM10 effect estimates which are a major basis for public health policies. In this study, temporal variability of short-term effects of PM10 for mortality was investigated from 2001 to 2015 in Seoul, Korea. A time series analysis was used to estimate the effects of PM10 on all-cause, circulatory, and respiratory mortality. Study period was divided into five years of moving time window, and relative risks and its 95% confidence intervals were estimated using distributed lag model for each time window. The annual average of PM10 in Seoul decreased from 52.6 μg/m3 in 2001 to 40.5 μg/m3 in 2015. There were significant temporal trends in PM10 effects regarding each mortality. The effects of PM10 tend to slightly increase in early of the study period and then dropped in recent years. An increase of 2 days of cumulative exposure of PM10 was associated with 0.31% (95% confidence interval: 0.15, 0.46) increase in all-cause mortality for 2001–2005 period, 0.61% (95% confidence interval: 0.34, 0.89) increase in 2005–2009 period, and −0.06% (95% confidence interval: −0.38, 0.25) increase in 2011–2015 period. We found evidence that short-term effects of PM10 on mortality have been changed over time in Seoul, Korea. The observed temporal trend might attributable from two factors which are change of PM10 compositions and population vulnerability. If effects of PM10 vary over time, considering its change would be necessary not only in the estimation of the PM10 effect but also its application for a variety of public health policies.

Early studies reported higher risk of COVID-19 outcomes for racial/ethnic minorities in the early phase of the pandemic in the United States. While the initial surge of COVID-19 was concentrated in some areas, COVID-19 became pervasive across the entire continent with high impacts in the northern region and central region in the end of 2020. With this geographical transition, we aim to investigate patterns of these racial/ethnic disparities over time. We assessed associations of percentage of race/ethnic minorities and racial segregation indexes with COVID-19 case and mortality rates in 3108 counties of the continental United States during the pandemic's early phase, second, and third phase (January 21–June 15, June 16–August 31, and September 1–December 18, 2020, respectively). We adjusted for population density, age, and sex. We tested whether time-varying associations were consistent across climate regions and explained by socioeconomic variables. In the early phase, counties with higher percentage of Black/African Americans and higher Black-White segregation had higher COVID-19 case and mortality rates. These associations decreased over time and reversed in the third phase. Associations between Hispanic and COVID-19 outcomes were positive in all periods, but more so early in the pandemic. Higher COVID-19 case rates for counties with higher non-Hispanic White population emerged in the third phase. These trends were similar across climate regions, and socioeconomic variables did not explain these trends. In summary, county-level racial/ethnic disparities of COVID-19 are not stationary but change over the course of the pandemic, suggesting complex social, cultural, and political influences., Graphical abstract Unlabelled Image

Although research indicates health and well-being benefits of greenspace, little is known regarding how greenspace may influence adaptation to health risks from heat, particularly how these risks change over time. Using daily hospitalization rates of Medicare beneficiaries ≥65 years for 2000-2016 in 40 U.S. Northeastern urban counties, we assessed how temperature-related hospitalizations from cardiovascular causes (CVD) and heat stroke (HS) changed over time. We analyzed effect modification of those temporal changes by Enhanced Vegetation Index (EVI), approximating greenspace. We used a two-stage analysis including a generalized additive model and meta-analysis. Results showed that relative risk (RR) (per 1 °C increase in lag0-3 temperature) for temperature-HS hospitalization was higher in counties with the lowest quartile EVI (RR=2.7, 95% CI: 2.0, 3.4) compared to counties with the highest quartile EVI (RR=0.40, 95% CI: 0.14, 1.13) in the early part of the study period (2000-2004). RR of HS decreased to 0.88 (95% CI: 0.31, 2.53) in 2013-2016 in counties with the lowest quartile EVI. RR for HS changed over time in counties in the highest quartile EVI, with RRs of 0.4 (95% CI: -0.7, 1.4) in 2000-2004 and 2.4 (95% CI: 1.6, 3.2) in 2013-2016. Findings suggest that adaptation to heat-health associations vary by greenness. Greenspace may help lower risks from heat but such health risks warrant continuous local efforts such as heat-health plans.

Additional file 1. Simulation methods and additional results of simulations. Figure S1. Two hypothetical observable lag patterns of the association between 1 μg/m3 increase of exposure to PM10 and mortality, used to generate simulation samples (the last row of panels). A) and B) Hypothetical actual effect (identical). C) Mortality displacement for a few days to several weeks for Observable lag pattern 1. D) Mortality displacement for a few days to several weeks for Observable lag pattern 2. E) Mortality displacement for few days to two years for Observable lag pattern 1. F) Mortality displacement for few days to two years for Observable lag pattern 2. G) Observable lag patterns 1. H) Observable lag pattern 2. Figure S2. An example of simulated samples. A) all-cause death series. B) PM10 series. Table S1. Bias and Standard Deviation for the Association between a Two-Day Moving Average of PM10 (lag0–1) and All-Cause Mortality Estimated by a Model with Different Adjustment Methods for Seasonality and Long-Term Time-Trend over 5000 Samples

Additional file 2. Real data analysis methods and additional results. Table S2. Percentage increase and 95% confidence intervals of cardiovascular mortality risk per a 10 μg/m3 increase in two-day moving average of PM10 (lag0–1) or one-year distributed lags of PM10 (lag0–365) in seven major cities of South Korea, 2006–2013. Table S3. Percentage increase and 95% confidence intervals of respiratory mortality risk per a 10 μg/m3 increase in two-day moving average of PM10 (lag0–1) or one-year distributed lags of PM10 (lag0–365) in seven major cities of South Korea, 2006–2013. Figure S3. Time-series of logarithm of daily all-cause deaths predicted by models with two different adjustments for seasonality and long-term time trend and their absolute differences. A) Time-series in Seoul, 2006–2013 B) Absolute differences in Seoul. C) Time-series in Daegu, 2006–2013. D) Absolute differences in Daegu. Grey vertical dotted lines in B) and D) denote 80th, 90th, and 98th percentile of absolute differences. Figure S4. Results of additional sensitivity analyses in seven major cities of South Korea, 2006–2013. A) All-cause mortality. B) Cardiovascular mortality. C) Respiratory mortality. Percentage increases estimated by models with NCS(doy, 20df)+I(year)+NCS(week, 3df)+NCS(month, 3df) in Table 3 (for all-cause mortality), Table S2 (for cardiovascular mortality), and Table S3 (for respiratory mortality) were duplicated for comparison (red triangles).

Concentration-response function for exposure to ambient particulate matter (PM) and mortality (i.e., relative risk, RR) may be inequal across communities by socioeconomic conditions. Investigation on specific mechanisms of this inequality regarding susceptibility to PM, beyond non-specific “socioeconomic conditions”, would provide policy-relevant implications for tackling this inequality. However, such investigation via epidemiological studies is challenged by residual confounding by correlated mechanisms and different loss of life expectancy by PM exposures between communities. Here, we aimed to assess community characteristics including different aspects of socioeconomic deprivation, medical resources, health behaviors, air quality, and greenness in their relation to inequal RR for PM10 and cause-specific mortality in 72 municipalities in South Korea, 2006–2013, considering these challenges. We found that a 10 μg/m3 increase in PM10 on average across 46 days was associated with a 1.05% (95% CI: 0.24, 1.88) increase in all-cause mortality (ALL), 1.32% (95% CI: -0.29, 2.95) increase in cardiovascular mortality (CVD), and 6.47% (95% CI: 3.06, 10.00) increase in respiratory mortality (RES). The association between PM10 and mortality was higher in communities with higher ratio of SO2 to PM10 (ALL and RES), higher material deprivation (ALL, CVD, and RES), lower medical resources (CVD), higher prevalence of drinking (ALL and CVD), and lower prevalence of smoking (CVD and RES). Lag-structures showed smaller loss of life expectancy by PM exposures in communities with higher prevalence of smoking. Our findings suggest that PM-related health inequalities are shaped by a variety of mechanisms relating to susceptibility to PM exposures and different loss of life expectancy. Health policies controlling community characteristics may contribute to minimizing PM10-related health inequalities in those perspectives.

Increased ambient concentration of particulate matters are considered as one of major causes for increased prevalence or exacerbation of asthma or asthma like symptoms. Recently, possible temporal variation in risks of PM on mortality has been suggested. We investigated short-term effect of both PM10 and PM2.5 on asthma hospitalization, and assessed temporal variation of PM risks in Seoul, Korea, 2003-2011. Generalized additive model was used to estimate PM risks on asthma hospitalization with consideration by long-term trend, influenza epidemic, day of week, meteorological factors. To assess temporal variation of PM risks, year-round PM risks were estimated. Stratified analysis by season and age-group were also conducted. Estimated RRs of PM on asthma hospitalization by an increase of 10㎍/㎥were 1.0084 (95% CI: 1.0041-1.0127) and 1.0156 (95% CI: 1.0055-1.0259) respectively with 7-days lag periods (lag06). PM2.5 had stronger effect than PM10 for all age group. Elderly group was most affected by PM. For the analysis of temporal variation of PM risks, we found increasing trend in total population and the elderly group. In the season-specific analysis, we also found increasing trend in winter for PM10, and in spring for PM2.5. PM10 and PM2.5 has adverse effect on asthma hospitalization with evidence suggesting temporal variation in PM risks. Further research will be needed to confirm the temporal variation of PM risk on asthma hospitalization, and to identify casual factors affecting this temporal variation. This study results could be evidentiary materials for establishing valid public health policies to reduce health burden or economic burden of asthma.

OBJECTIVES: Epidemiological evidence of associations between ambient particulate matter (PM) and tuberculosis (TB) risk is accumulating. Two previous studies in Korea found associations between air pollution—especially sulfur dioxide (SO2)—and TB. In this study, we conducted an annual time-series cross-sectional study to assess the effect of PM with an aerodynamic diameter less than 10 μm (PM10) on TB risk in seven major cities of Korea from 2010 to 2016, taking into account time lag and long-term cumulative exposure.METHODS: Age-standardized TB notification rates were derived using the Korea National TB Surveillance System. Annual average PM10 concentrations were obtained from annual Korean air quality reports. We applied a generalized linear mixed model with unconstrained distributed lags of exposure to PM10. We adjusted for potential confounders such as age, health behaviors, and area-level characteristics.RESULTS: Both average annual PM10 concentrations and age-standardized TB notification rates decreased over time. The association between cumulative exposure to PM10 and TB incidence became stronger as a longer exposure duration was considered. An increase of one standard deviation (5.63 μg/m3) in PM10 exposure for six years was associated with a 1.20 (95% confidence interval, 1.17 to 1.22) times higher TB notification rate. The marginal association of exposure duration with the TB notification rate was highest at four and five years prior to TB notification. This association remained consistent even after adjusting it for exposure to SO2.CONCLUSIONS: The findings of this study suggest that cumulative exposure to PM10 may affect TB risk, with a potential lag effect.

This study aims to investigate the association of particulate matter with an aerodynamic diameter smaller than 10 &mu, m (PM10) and greenness with cause-specific mortality and their interactions in seven Korean metropolitan cities. We obtained the annual standardized cause-specific mortality rates, annual mean concentration of PM10, and annual Normalized Difference Vegetation Index (NDVI) for 73 districts for the period 2008&ndash, 2016. We used negative binomial regression with city-specific random effects to estimate the association of PM10 and greenness with mortality. The models were adjusted for potential confounders and spatial autocorrelation. We also conducted stratified analyses to investigate whether the association between PM10 and mortality differs by the level of greenness. Our findings suggest an increased risk of all causes examined, except respiratory disease mortality, with high levels of PM10 and decreased risk of cardiovascular-related mortality with a high level of greenness. In the stratified analyses, we found interactions between PM10 and greenness, but these interactions in the opposite direction depend on the cause of death. The effects of PM10 on cardiovascular-related mortality were attenuated in greener areas, whereas the effects of PM10 on non-accidental mortality were attenuated in less green areas. Further studies are needed to explore the underlying mechanisms.

An increasing number of studies have suggested benefits of greenness exposure on mental health. We examined the association between urban greenness and depressive symptoms in adults in the general population living in the seven major cities in Korea (N = 65,128). Using data from the Korean Community Health Survey 2009, depressive symptoms were measured on the Center for Epidemiological Studies Depression Scale (CES-D). Greenness was assessed using Normalized Difference Vegetation Index (NDVI) and land-use data (forest area and forest volume). Logistic regression models were fitted to adjust for potential confounders. Individuals in regions with the highest NDVI (quartile 4) had the lowest odds for depressive symptoms compared to quartile 1, after adjusting for potential confounders (OR = 0.813, 95% CI: 0.747, 0.884). For all greenness indicators except for forest area per district area (%), the highest rate of depressive symptoms was found for the individuals in the lowest quartile of greenness (quartile 1) and the lowest rate of depressive symptoms for those in the highest quartile of greenness (quartile 4). We found an inverse association between urban greenness and depressive symptoms, which was consistent across a variety of greenness indicators. Our study suggests health benefits of greenness and could provide a scientific basis for policy making and urban planning.

The United States has the highest numbers of confirmed cases and deaths during the novel coronavirus disease 2019 (COVID-19) pandemic. Previous studies reported that urban residents are more vulnerable to the spread and mortality of COVID-19 than rural residents. However, the pathways through which urban environments affect COVID-19 spread and mortality are unclear. We collected daily data on the number of confirmed cases and deaths of COVID-19 from Mar. 01 to Nov. 16, 2020 for all 91 counties in New York, New Jersey, and Connecticut in the United States. We calculated the COVID-19 incidence %, daily reproduction number, and mortality %, then estimated the associations with urban environment indicators using regression models. COVID-19 outcomes were generally highest in areas with high population density, and this pattern was evident in the early period of epidemic. Among the area-level demographic variables, the percentage of Black or Hispanic residents showed the strongest positive association with COVID-19 outcomes. Higher risk of COVID-19 outcomes was also associated with higher percentage of overcrowded households, uninsured people, and income inequality. The percent elderly, sex ratio (the ratio of males to females), and greenness were negatively associated with risk of COVID-19 outcomes. The results of this study could indicate where resources are most needed., Graphical abstract Unlabelled Image

Evidence from cohort studies on the effects of long-term exposure to air pollution on mortality is limited in South Korea, which has high concentration of particles compared to North America, Western Europe, and Japan, and low exposure compared to China. To reduce knowledge gaps between other countries and South Korea, we investigated the association between all-cause, cardiovascular, and respiratory mortality and long-term exposure to PM10 and, as a surrogate for fine particles from local emission sources, SO2 and NO2. Participants comprised 18,220 subjects (97,114.4 person-years) residing in 73 districts of seven major cities of South Korea who were assigned to measurements of fixed-site monitoring stations and followed up. We applied Cox proportional hazard models with time-varying exposure up to three years average of air pollutants. We adjusted for individual and district-level covariates measured at baseline such as age, sex, socioeconomic positions, and health behaviors. We found that hazard ratios of PM10 and SO2 for all-cause mortality leveled off over approximately 5 ppb of SO2 and 35–50 μg/m3 of PM10. Interquartile range increases of PM10 (5.05 μg/m3), SO2 (2.09 ppb), and NO2 (11.41 ppb) were associated with 14.4% (95% CI: −0.4, 31.4), 18.1% (−4.5, 46.0), and 18.9% (−8.7, 54.7) increases in cardiovascular mortality, respectively. We did not find positive associations for respiratory mortality. The increase in cardiovascular mortality varied by sex (for PM10, in females, 27.4% (5.8, 53.5) increase), smoking (in non-smokers, 35.9% (12.7, 64.0) increase), drinking (in drinkers, 24.5% (2.1, 51.8) increase), marital status (in those not married, 23.1% (1.1, 49.9)), employment status (for SO2, in those employed, 79.4% (16.1, 177.3) increase), body mass index (in those ≥23, 47.6% (10.4, 97.3) increase), and community deprivation (for PM10, in less deprived communities, 21.0% (1.3, 44.4) increase). In summary, long-term exposure to air pollution is associated with mortality risk in South Korea. Our results suggest that the health effect of long-term exposure to air pollution may not be equal by sex, health behaviors and socioeconomic positions.

To control the novel coronavirus disease (COVID-19) outbreak, state and local governments in the United States have implemented several mitigation efforts that resulted in lower emissions of traffic-related air pollutants. This study examined the impacts of COVID-19 mitigation measures on air pollution levels and the subsequent reductions in mortality for urban areas in 10 US states and the District of Columbia. We calculated changes in levels of particulate matter with aerodynamic diameter no larger than 2.5 μm (PM2.5) during mitigation period versus the baseline period (pre-mitigation measure) using the difference-in-difference approach and the estimated avoided total and cause-specific mortality attributable to these changes in PM2.5 by state and district. We found that PM2.5 concentration during the mitigation period decreased for most states (except for 3 states) and the capital. Decreases of average PM2.5 concentration ranged from 0.25 μg/m3 (4.3%) in Maryland to 4.20 μg/m3 (45.1%) in California. On average, PM2.5 levels across 7 states and the capital reduced by 12.8%. We estimated that PM2.5 reduction during the mitigation period lowered air pollution-related total and cause-specific deaths. An estimated 483 (95% CI: 307, 665) PM2.5-related deaths was avoided in the urban areas of California. Our findings have implications for the effects of mitigation efforts and provide insight into the mortality reductions can be achieved from reduced air pollution levels., Graphical abstract Unlabelled Image, Highlights • PM2.5 levels during COVID-19 mitigation decreased across 7 states and the capital. • PM2.5 reduction during COVID-19 mitigation is estimated to lowered air pollution-related deaths. • Findings have implications for the indirect health effects of mitigation efforts. • Results have implications for potential health benefits from air pollution policy.

In 2005, “The Master Plan for Atmospheric Environment Management in Capital Area” is established to reduce ambient concentration of air pollutants. Based on the plan, environmental poli...

Background: Asian Dust (AD) is a natural source of particulate matter (PM) in Korea and increase the concentration of PM remarkably. However, the characteristics of PM from AD differ from that of P...