High Levels Of PM2 Research Articles

Identification and Characterization of PM2.5 Emission Sources in Shanghai during COVID-19 Pandemic in the Winter of 2020

The novel coronavirus (COVID-19) epidemic broke out in Wuhan at the end of 2019 and spread around the whole of China in 2020. In order to reduce the spread of COVID-19, transportation and industrial activities in different regions were limited to varying degrees. This study uses bivariate concentration polar plots, integrated with k-means clustering and temporal variation analyses for PM2.5 time series data, to understand the PM2.5 source characteristics in Shanghai during the COVID-19 pandemic in the winter of 2020. Our findings show that 34.33% of the PM2.5 particles arise from external sources while 65.67% are from local sources. The results of source apportionment combined with land use, wind speed, and direction data are further used to locate the most likely directions of different source categories and geographic origins of PM2.5. During the lockdown period in 2020, traffic and industrial activity were still primary local sources of PM2.5 emissions in Shanghai. The growth of motor vehicle ownership, limited public transport, and a large volume of freight transport in Shanghai result in a higher level of PM2.5 concentrations on weekends than in midweeks. On the other hand, the regional-scale transport of air pollutants from the Yangtze River Delta, the Central Plains, the inland area of northern China, and coastal cities in the north and south of Shanghai aggravates PM2.5 pollution in Shanghai under unfavorable meteorological conditions. The methods and results presented here lay a basis for further study on the complicated effects of meteorological and anthropogenic factors on PM2.5 pollution and on the development of detailed and urgent strategies for the improvement of air quality.

Spatio-temporal association mining of intercity PM2.5 pollution: Hubei Province in China as an example.

The complex interaction between emissions, meteorology, and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can reveal potential laws, providing new possibilities for understanding the evolution and causes of air pollution. Based on the Granger method and block modeling analysis, this paper explored the intercity spillover effects of hourly PM2.5 in Hubei Province, China, to determine the specific role (i.e., overflow, limited overflow, bilateral, inflow, and limited inflow) of each city on regional pollution formation. Furthermore, a dynamic Apriori algorithm considering time-lag effects was used to mine the spatio-temporal associations of extreme PM2.5 pollution events among different cities. Results suggest that the northern and central cities with high-level PM2.5 concentration in Hubei have a significant spillover effect, whereas the eastern and southern cities generally play a role as the sink of pollutants. Based on the association rules of extreme PM2.5 pollution, four main pollutant transport channels were excavated and well matched with the trajectories extracted by the atmospheric model. This paper provides new insights for exploring the interaction of intercity particulate pollution, which is a supplement and cross-validation of the model results.

Spatiotemporal Trends and Influencing Factors of PM2.5 Concentration in Eastern China from 2001 to 2018 Using Satellite-Derived High-Resolution Data

Ambient exposure to fine particulate matter (PM2.5) in eastern China, a densely populated region with very high-level PM2.5 pollution, has attracted great concern from the public, government, and scientific community. By taking advantage of advanced statistical methods and a high-resolution PM2.5 dataset, this study explicitly investigated the spatiotemporal changes in PM2.5 in eastern China from 2001 to 2018 at multiple spatial and temporal scales and examined its links with natural and socioeconomic factors to explore their effects on PM2.5 changes. This study found that the PM2.5 concentration in most of eastern China declined recently, while most of the discernable decreasing trends occurred in the southern and western areas of the study domain, and the statistically significant increasing trends were primarily in the North China Plain. The influencing factors analysis found that, among the selected four natural and five anthropogenic factors, temperature, and population density exerted more potent effects than the other influencing factors, and all the influencing factors were found to impose complex effects on the PM2.5 concentration over space and time. Our study draws a complete picture of the changes in PM2.5 and its possible influences, which could guide future actions to mitigate PM2.5 pollution in eastern China.

Intrauterine and Extrauterine Environmental PM2.5 Exposure Is Associated with Overweight/Obesity (O/O) in Children Aged 6 to 59 Months from Lima, Peru: A Case-Control Study.

There is evidence that PM2.5 could be obesogenic. Lima is one of the most polluted cities in South America, with an increasing prevalence of childhood obesity. This study aimed to determine the association between PM2.5 exposure of children aged 6 to 59 months and being overweight or obese (O/O) in a significant dataset survey. Cases were defined when weight for height Z-score (WHZ) was >2 standard deviations (SD) from the mean, for each sex. A control was defined when WHZ was between ±2 SD. We used a conditional logistic regression model to calculate the odds ratio (OR) between extrauterine and intrauterine PM2.5 exposure and O/O. Extrauterine PM2.5 exposure was evaluated as a 6-month PM2.5 mean prior to the survey. We found a significant association between O/O and extrauterine (OR: 1.57, 1.51–1.63) and intrauterine (OR: 1.99, 1.88–2.12) PM2.5 exposure for an increment of 10 μg/m3. The ORs increased as the quartile increased in both exposures. We observed a higher association in children aged 6–11 months (OR: 3.07, 2.84–3.31). In conclusion, higher levels of PM2.5 in Lima and Callao were associated with cases of O/O in children from 6 to 59 months, with the association higher for prenatal exposure.

The impact of land-sea breezes on the formation of Brown haze in an urban isthmus environment

In some international cities, winter air pollution can manifest into a local-scale brown air pollution haze which has been associated with negative health outcomes. Land-sea breezes are known to impact urban air quality through the recirculation of air pollution and the formation of internal boundary layers (IBLs). However, research into land-sea breezes has primarily focused on summer air pollution and little is known about the influence of land-sea breezes on local-scale winter brown haze. Using continuous data (including surface meteorology, surface air quality, satellite-derived sea surface temperatures, and ceilometer-derived boundary-layer depths) observed over seven winters from 2013 to 2019, we present a novel investigation of the influence of land-sea breezes on brown haze in Auckland, New Zealand. Severe brown haze days are significantly more likely to coincide with a land-sea breeze circulation simultaneously occurring at both the east and west coasts when compared with days on which brown haze is expected but not observed (based on favourable meteorology and high surface air pollution levels). Both severe brown haze and high surface level PM2.5 concentrations (previously associated with the presence of brown haze) are found to be associated with a high degree of horizontal recirculation at Auckland's east coast. No relationship is found between the occurrence of sea-breeze-induced IBLs and the formation of brown haze. The results presented in this study offer insights into the physical mechanisms that influence the formation and persistence of local-scale winter brown haze in a complex coastal setting with correspondingly complex land-sea breezes.

Effect of Short- to Long-Term Exposure to Ambient Particulate Matter on Cognitive Function in a Cohort of Middle-Aged and Older Adults: KoGES.

Exposure to ambient air pollution and its threat to human health is a global concern, especially in the elderly population. Therefore, more in-depth studies are required to understand the extent of the harmful effects of particulate matter (PM) based on duration and levels of exposure. An investigation was conducted to determine the association between short- (1–14 days), medium- (1, 3, and 6 months), and long-term (1, 2, and 3 years) exposure to air pollutants (PM2.5 and PM10) and cognitive function among Koreans (4175 participants, mean age 67.8 years, 55.2% women) aged over 50 years. Higher levels of PM2.5 exposure for short to long term and PM10 exposure for medium to long term were found to be associated with decreased cognitive function, as indicated by lower scores of the Mini-Mental State Examination adopted in Korean (K-MMSE). There were significant effect modifications by sex, age group, alcohol consumption, physical activity, and smoking status in the association between long-term PM2.5 and PM10 exposure and cognitive function. These findings, which underscore the importance of the efforts to reduce the exposure levels and durations of air pollutants, especially in the vulnerable elderly population, provide evidence for establishing more stringent policies for air pollution regulations.

Mortality and morbidity of chronic kidney disease associated with ambient environment in metropolitans in Taiwan

BackgroundHealth effects associated with extreme temperature and elevated air pollutants have been concerns. The present study examined mortality from and morbidity of chronic kidney disease (CKD) associated with extreme temperature and exposure to fine particulate matter (PM2.5) and ozone (O3) by sex and age in 2005–2016 in metropolitans of Taiwan. MethodsThe distributed lag non-linear model was applied to analyze roles of extreme high (99th percentile) and low (5th percentile) temperatures, and 90th percentile PM2.5 and ozone (O3) in association with CKD risks of deaths, emergency room visits (ERVs), and outpatient visits by age (40–64 and 65 years and above) and sex. Pooled relative risk (RR) and 95% confidence intervals (CI) for all studied areas were estimated using random-effects meta-analysis. ResultsCold spells (<14 °C) showed a higher risk on mortality from CKD for the elderly. Middle-aged population was more vulnerable to high temperature (31.3 °C) with the highest risk for women admitted to outpatient visits [RR = 1.25; (95% CI; 1.17–1.34)]. Women aged 65 years and above had the highest risk after exposing to higher levels of PM2.5 (55 μg/m3) [RR = 1.07; (95% CI; 1.03–1.12)] and O3 (43 ppb) [RR = 1.07; (95% CI; 1.00–1.15)]. ConclusionsThe elderly CKD patients were more prone to the adverse effect of low temperature and high levels of PM2.5 and O3. Middle aged groups were more prone to health risks related to the high temperature. Men are more susceptible to the high temperature, meanwhile women are sensitive to higher levels of PM2.5 and O3.

Long-term trends of atmospheric hot-and-polluted episodes (HPE) and the public health implications in the Pearl River Delta region of China

Air pollution and extreme heat have been responsible for more than a million deaths in China every year, especially in densely urbanized regions. While previous studies intensively evaluated air pollution episodes and extreme heat events, a limited number of studies comprehensively assessed atmospheric hot-and-polluted-episodes (HPE) – an episode with simultaneously high levels of air pollution and temperature – which have potential adverse synergic impacts on human health. This study focused on the Pearl River Delta (PRD) region of China due to its high temperature in summer and poor air quality throughout a year. We employed geostatistical downscaling to model meteorology at a spatial resolution of 1 km, and applied a machine learning algorithm (XGBoost) to estimate a high-resolution (1 km) daily concentration of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) and ozone (O3) for June to October over 20 years (2000–2019). Our results indicate an increasing trend (∼50%) in the frequency of HPE occurrence in the first decade (2000–2010). Conversely, the annual frequency of HPE occurrence reduced (16.7%), but its intensity increased during the second decade (2010–2019). The northern cities in the PRD region had higher levels of PM2.5 and O3 than their southern counterparts. During HPEs, regional daily PM2.5 exceeded the World Health Organization (WHO) and Chinese guideline levels by 75% and 25%, respectively, while the O3 exceeded the WHO O3 standard by up to 69%. Overall, 567,063 (95% confidence interval (CI): 510,357–623,770) and 52,231 (95%CI: 26,116–78,346) excessive deaths were respectively attributable to exposure to PM2.5 and O3 in the PRD region. Our findings imply the necessity and urgency to formulate co-benefit policies to mitigate the region's air pollution and heat problems.

Comprehensive chemical characterization of PM2.5 in the large East Mediterranean-Middle East city of Beirut, Lebanon

The chemical composition of PM2.5 at two sites in Lebanon, a country in the East Mediterranean - Middle East region, is investigated in the spring and summer seasons. The average PM2.5 concentrations were of (29 ± 16) µg/m3 for Beirut urban site and (32 ± 14) µg/m3 for Beirut suburban site. This study showed that the geographic location of the East Mediterranean region, such as its proximity to the Mediterranean Sea and the dust storm intrusion are a significant contributor to the high PM levels from natural sources, which cannot be mitigated, rendering the PM2.5 WHO annual Air Quality guideline unattainable due to high natural background, which also applies to the entire Middle East region. Turkey and Eastern Europe are the dominant origin of air masses throughout our sampling days, suggesting the long-range transport as an important potential contributor to the high observed concentrations of V, Ni, and sulfate in this region most probably in other East Mediterranean countries than Lebanon too. Main local sources determined through the chemical speciation including organics are road transport, resuspension of dust and diesel private generators. A health risk assessment of airborne metals was performed and the carcinogenic risk for all the metals exceeded by 42 (adults) and 14 (children) times the acceptable risk level (10−6) at both sites. Vanadium was the predominant carcinogenic metal, emphasizing the need to replace energy production with cleaner energy on a regional level and highlighting the severe impact of air pollution on the health of inhabitants in this region's main cities.

Particulate PCDD/F size distribution and potential deposition in respiratory system from a hazardous waste thermal treatment process

The particulate polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) of various sizes produced from the waste incinerators might have different toxicities, deposition characteristics, and potential health effects in the respiratory system, and their total toxicity equivalent (TEQ) concentration has been strictly regulated in recent years. There is a knowledge gap on the effects of air pollution control devices on particle size distributions (PSDs) of PCDD/Fs and their TEQ deposition. A hazardous waste thermal treatment plant equipped with an advanced scrubber, a cyclone demister, and activated carbon adsorption coupled with a baghouse filtration was investigated in this study. An 8-stage impactor was used to collect the particle distribution of PM10 and bounded PCDD/Fs from the gas stream at four sampling points located before and after each control unit. A “TEQDE” index is defined for the toxicity deposition of PM10-PCDD/F in the respiratory system. The advanced scrubbers significantly reduced the PM10-PCDD/F levels, especially for those with sizes ≥0.6 and ≤ 0.4 μm. Additionally, the cyclone also showed a better performance than the general dry gas treatment but had an efficiency drop with 1.5–4 μm particles. The PM10-PCDD/F loads in the final adsorption-filtration unit were eased and effectively removed the PM10-PCDD/Fs to sizes ≤0.5 or≥1.5 μm. The total TEQDE was 0.00052 ng WHO-TEQ Nm−3 and had a peak level of 0.000157 ng WHO-TEQ Nm−3 at 1.2 μm. PSDs were more sensitive to the PSDs of PM mass at high PM levels but strongly correlated with the PSDs of “PM10-PCDD/Fs/PM10” at low PM10 loads. Consequently, the advanced control system could effectively remove the PM10-PCDD/Fs and might extend the adsorption-filtration lifetime. However, the PM10-PCDD/Fs ≤ 0.4 μm had a higher TEQ deposition rate and should be further considered in emissions and ambient air quality evaluations.

Remote sensing estimation of surface PM2.5 concentrations using a deep learning model improved by data augmentation and a particle size constraint

Accurate estimation of PM2.5 concentrations is critical to understanding and counteracting air pollution. In the past decade, various machine learning models, especially deep learning models, have been widely used in PM2.5 remote sensing estimation and have achieved remarkable performance. However, a typical pitfall of deep learning models is the problem of high-value underestimation, i.e., the models often underestimate high-level PM2.5 concentrations. Alleviating the problem of high-value underestimation and improving the estimation accuracy of high PM2.5 concentrations are crucial. This study developed a new deep learning model that combines data augmentation and a particle size constraint to improve the high-level PM2.5 estimation. Based on the residual neural network (ResNet), this study used random oversampling to construct a data-augmented deep residual learning model (AugResNet). In addition, a deep residual neural network model with a particle size constraint was established and called ConResNet. Then, the data augmentation and particle size constraint were incorporated into the deep residual neural network model (denoted as Aug_ConResNet). We evaluated the above four models across China with the 10-fold site-based cross-validation approach. In terms of the estimation accuracy for high PM2.5 concentrations (>75 μg/m3), AugResNet (R2 = 0.813, RMSE = 19.152 μg/m3), ConResNet (R2 = 0.796, RMSE = 20.841 μg/m3) and Aug_ConResNet (R2 = 0.820, RMSE = 18.810 μg/m3) outperformed ResNet (R2 = 0.780, RMSE = 21.628 μg/m3). Results showed that the data augmentation and particle size constraint alleviated the problem of high-value underestimation and improved the estimation accuracy for high PM2.5 concentrations. The accurate estimation of high PM2.5 concentrations has important application potential for remote sensing monitoring of polluted weather.

Air pollution and green spaces in relation to breast cancer risk among pre and postmenopausal women: A mega cohort from Catalonia

BackgroundThe association between air pollution and green spaces with breast cancer risk stratified by menopausal status has not been frequently investigated despite its importance given the different impact of risk factors on breast cancer risk depending on menopausal status. ObjectivesTo study the association between air pollution, green spaces and pre and postmenopausal breast cancer risk. MethodsWe conducted a population-based cohort study using electronic primary care records in Catalonia. We included women aged 17–85 years free of cancer at study entry between 2009 and 2017. Our exposures were particulate matter <2.5 μm (PM2.5) & <10 μm (PM10), nitrogen dioxide (NO2), normalized difference vegetation index (NDVI), and percentage of green spaces estimated at the census tract level. Breast cancer was identified with ICD-10 code C50. We estimated cause-specific hazard ratios (HR) for the relationship between each individual exposure and pre and postmenopausal breast cancer risk, using linear and non-linear models. ResultsOf the 1,054,180 pre and 744,658 postmenopausal women followed for a median of 10 years, 6,126 and 17,858 developed breast cancer, respectively. Among premenopausal women, only very high levels of PM10 (≥46 μg/m3) were associated with increased cancer risk (compared to lower levels) in non-linear models. Among postmenopausal women, an interquartile range increase in PM2.5 (HR:1.03; 95%CI:1.01–1.04), PM10 (1.03; 1.01–1.05), and NO2 (1.05; 1.02–1.08) were associated with higher cancer risk. NDVI was negatively associated with decreased cancer risk only among postmenopausal women who did not change residence during follow-up (0.84; 0.71–0.99) or who were followed for at least three years (0.82; 0.69–0.98). DiscussionLiving in areas with high concentrations of PM2.5, PM10, and NO2 increases breast cancer risk in postmenopausal women while long-term exposure to green spaces may decrease this risk. Only very high concentrations of PM10 increase breast cancer risk in premenopausal women.

Habitual exercise, chronic exposure to fine particulate matter and high-sensitivity C reactive protein in Asian adults

ObjectivesExercise may increase the inhaled amount of air pollutants and exacerbate the adverse health effects. We investigated the combined effects of chronic exposure to fine particulate matter with an aerodynamic...

Exposure to Ambient Air Pollutant PM10 in the Second Trimester of Pregnancy Is Associated with Preterm Birth: A Birth-Based Health Information Cohort Study.

Objectives We evaluated the effects of exposure to high concentrations of particulate matter (PM)10 on preterm birth (PTB) and identified a critical concentration of PM10 that could lead to PTB via a birth-based health information cohort study. Methods We conducted a birth-based cohort study consisting of nonanomalous singleton births at 22-42 weeks. PTB was defined as babies born alive before 37 weeks of pregnancy. Pregnancy period exposure averages were estimated for PM10 based on the China National Environmental Monitoring Centre (CNEMC). Pregnant women who lived within 50 km of the monitor station were recruited into this study. Logistic regression analyses were performed to determine the association between PTB and exposure to PM10 at different pregnancy periods with adjustment for confounding factors. Results The relative frequency of PTB was 8.7% in the study cohort of 5,291 singleton live births. A total of 1137 women had a high level of PM10 exposure (≥60 μg/m3) in the second trimester of pregnancy. The average concentrations of PM10 in the first, second, and third trimesters of pregnancy and throughout pregnancy were 53.8 μg/m3, 54.2 μg/m3, 55.6 μg/m3, and 54.3 μg/m3, respectively. The generalized additive model (GAM) analysis showed that there was a nonlinear correlation between PM10 and PTB in the second trimester of pregnancy (P < 0.001). The adjusted odds ratio between PTB and low concentration PM10 exposure (PM10 < 60 μg/m3) in the second trimester of pregnancy was 1.01 (95% CI 0.95-1.05). However, high PM10 exposure (PM10 ≥ 60 μg/m3) in the second trimester of pregnancy had an increased PTB risk even after adjustment for coexisting risk factors with an adjusted odds ratio of 1.78 (95% CI 1.69-1.87), and the incidence of PTB increased with an increase in PM10 exposure. Conclusions Our research discovered that exposure to high levels of PM10 increases the risk of PTB and the second trimester is the most vulnerable gestational period to ambient air pollution exposure. PM10 concentrations more than 60 μg/m3 are detrimental to pregnant women in their second trimester. This study has implications for health informatics-oriented healthcare decision support systems.

Unbalanced PM2.5 emission and happiness effect through cross-regional trade in China

Cross-regional trade and environmental impact are intricately linked. While the PM2.5 emissions embodied in trade has been discussed previously, an integrated analysis of how cross-regional trade reallocate PM2.5 emission flows and further impact residents' happiness has, to date, rarely been explored. Based on emission inventory and multiregional input-output model, the production-, consumption-, and income-based PM2.5 emissions were estimated, combined with PM2.5 Gini coefficient the regional PM2.5 emissions inequalities as well as the happiness effect were investigated. The results show that 49.6% and 28.1% of the PM2.5 emissions were driven by final demands and primary inputs of other provinces, which would displace local residents' happiness effect from the final consumers. The PM2.5 Gini coefficient for consumption-based emission was lower than that for production- and income-based emission, indicating the inequality of regional distribution of PM2.5 emissions in China has been exacerbated by production- and income-based accounting. We found that for every one-unit increase in the consumption-based PM2.5 emission was associated with a 0.037 and 0.072 decrease in happiness and depression index, respectively. The higher-income north provinces with high levels of PM2.5 footprint had a significant negative impact on both happiness and depression sentiment due to much higher resource intensities. Those results were partly attributed to the variation of economic structure and further highlight the critical role of consumption-based measurement in evaluating unbalanced PM2.5 emissions and sentiment impacts, promoting final consumers share environmental responsibility by tracing their transferred air pollution.

PM2.5 exposure differences between children and adults

Heights of children and adults vary substantially and may cause different exposure to PM2.5 particles. We measured pedestrian PM2.5 exposure by foot following a 5.5 km route along kindergartens and schools in Mainz (Germany). Measurements were conducted in November 2019 on eight consecutive days at two heights, the potential breathing heights of adults in 1.6 and children in 1.0 m using Alphasense OPC-N3 low-cost sensors. We found that regardless of height level, persisting calm weather conditions including low wind speeds <1.0 m/s and lack of precipitation lead to increased PM2.5 exposure exceeding 67.8 μg/m3. Comparing the height levels revealed that the children were exposed to >20% higher concentrations on six days (p < 0.01), on a single day this difference exceeded 80% (24.7 μg/m3). Differences generally increased with increasing PM2.5 concentrations, though the latter are largely independent of the position along the route but varied strongly among days. These findings are supported by a microclimate simulation including traffic exhaust emissions revealing strongest height differences nearby streets with high traffic intensities. Our results demonstrate that children are exposed to considerably higher levels of PM2.5 that are typically not observed in the stationary networks recording aerosols on only one level.

Effect of high-level fine particulate matter and its interaction with meteorological factors on AECOPD in Shijiazhuang, China

Epidemiological evidence of the effect of high-level air pollution and its interaction with meteorological factors on the risk of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is limited. Daily data on AECOPD cases, air pollutants and meteorological factors were collected from 2015 to 2018 in Shijiazhuang. A distributed lag non-linear model (DLNM) was used to explore the lag and cumulative effect of PM2.5 on the risk of AECOPD. The effect of the interaction between PM2.5 and meteorological factors on AECOPD was estimated by a generalized additive model (GAM) and a stratification model. A total of 4766 patients with AECOPD were enrolled. After controlling for confounders, each 10 μg/m3 increase in PM2.5 led to a 5.8% increase in the risk of AECOPD on day lag 0. The cumulative effect of PM2.5 on AECOPD risk showed an increasing trend after 3 days. Similar results were observed in both smoking and non-smoking patients. There was an interaction between PM2.5 and meteorological factors, and the risk of AECOPD was higher in cold and lower humidity conditions than in other conditions. High-level PM2.5 exposure is positively associated with the risk of AECOPD onset, and the effect of PM2.5 can be modified by the temperature and relative humidity. Public health guidelines should pay close attention to AECOPD risk under the condition of high-level PM2.5 with low temperature or low humidity.

No Country for Old Men. Assessing Socio-Spatial Relationships Between Air Quality Perceptions and Exposures in Southern Chile.

An extensive body of research has been noted that many socially deprived communities tend to live in areas characterized by higher levels of outdoor air pollution. Whilst there is an expanding literature documenting this disproportionate distribution, most previous studies have taken place in the Global North, have focused with industrial or vehicle air pollution sources and have tend to ignore the complex interactions between exposures, public perceptions and social factors. In this paper, we investigate the social vulnerability to and risk perceptions of air pollution sourced from domestic heating in two Chilean cities with particularly high levels of PM2.5 during winter months. To this end, we integrate primary survey data, with geographically detailed estimates of air pollution exposures and area-level characteristics obtained from the Chilean Census. We first examine the spatial distribution of PM2.5 exposures and air quality perceptions, and subsequently explore relationships between socio-demographic characteristics, air pollution exposure, and health concerns. Our results revel evident spatial patterns of dispersion, with some neighborhoods being more polluted than others. Age and percentage of roofs in poor condition in the participant's census tract are the best predictors of PM2.5 exposure. We find no correlation between perceived and real levels of contamination. Our multivariate analysis indicates that personal perceptions of air quality are significantly associated with age, gender, family structure, and heating behaviors. Such detailed depictions provide insights into potential meaningful strategies to improve air quality and highlight the need to incorporate measures to better protect older adults.

Relationship between meteorological factors, air pollutants and hand, foot and mouth disease from 2014 to 2020

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.

Observational studies: Ambient air pollution and hospitalization for RA-ILD in a heavily polluted city in China.

Little is known within the medical community about the impact of air pollution on hospital admissions due to rheumatoid arthritis associated with interstitial lung disease (RA-ILD). Our research aimed to explore whether there is a correlation and to estimate how the association was distributed across various lags in Jinan, China.The relationships between ambient air pollutant concentrations, including PM2.5, PM10, sulfur dioxide (SO2), ozone (O3), and nitrogen dioxide (NO2), and monthly hospitalizations for RA-ILD were studied by employing a general linear model with a Poisson distribution. This time-series study was performed from January 1st, 2015 to December 31st, 2019.In the 5-year study, there were 221 hospitalizations for RA-ILD in Jinan city. The levels of PM2.5, PM10, SO2, and NO2 were significantly related to the number of admissions for RA-ILD. PM2.5, PM10, and SO2 showed the most significant effect on the month (lag 0), and NO2 was most related to RA-ILD at a lag of two months (lag 2). The monthly admissions of RA-ILD increased by 0.875% (95% CI: 0.375–1.377%), 0.548% (95% CI: 0.148–0.949%), 1.968% (95% CI: 0.869–3.080%), and 1.534% (95% CI: 0.305–2.778%) for each 10 μg/m3 increase in PM2.5, PM10, SO2 and NO2, respectively.This study might add more detailed evidence that higher levels of PM2.5, PM10, SO2 and NO2 increase the risk of hospitalizations for RA-ILD. Further study of the role of air pollution in the pathogenesis of RA-ILD is warranted.