The burden associated with ambient PM2.5 and meteorological factors in Guangzhou, China, 2012-2016: A generalized additive modeling of temporal years of life lost

Effects of weather and air pollution on outpatient visits for insect-and-mite-caused dermatitis: an empirical and predictive analysis

BACKGROUND

Dermatitis caused by insects and mites, diagnosed as papular urticaria or scabies, is a common skin disease. However, there is still a lack of studies about the effects of weather and air pollution on outpatient visits for this disease. This study aims to explore the impacts of meteorological and environmental factors on daily visits of dermatitis outpatients.

METHODS

Analyses are conducted on a total of 43,101 outpatient visiting records during the years 2015-2020 from the largest dermatology specialist hospital in Guangzhou, China. Hierarchical cluster models based on Pearson correlation between risk factors are utilized to select regression variables. Linear regression models are fitted to identify the statistically significant associations between the risk factors and daily visits, taking into account the short-term effects of temperatures. Permutation importance is adopted to evaluate the predictive ability of these factors.

RESULTS

Short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. In terms of total outpatients, the one-day lagged temperature not only has a significant impact on daily visits, but also has the highest median value of permutation importance. This conclusion is robust across most subgroups except for subgroups of summer and scabies, wherein the three-day lagged temperature has a negative effect. By contrast, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. Moreover, weekdays, holidays and trends have significant impacts on daily visits, but with weak predictive abilities.

CONCLUSIONS

Our study suggests that short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. Nevertheless, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.

Long-Term Air Pollution Characteristics and Multi-scale Meteorological Factor Variability Analysis of Mega-mountain Cities in the Chengdu-Chongqing Economic Circle

Currently, air quality has become central to global environmental policymaking. As a typical mountain megacity in the Cheng-Yu region, the air pollution in Chongqing is unique and sensitive. This study aims to comprehensively investigate the long-term annual, seasonal, and monthly variation characteristics of six major pollutants and seven meteorological parameters. The emission distribution of major pollutants is also discussed. The relationship between pollutants and the multi-scale meteorological conditions was explored. The results indicate that particulate matter (PM), SO2 and NO2 showed a "U-shaped" variation, while O3 showed an "inverted U-shaped" seasonal variation. Industrial emissions accounted for 81.84%, 58% and 80.10% of the total SO2, NOx and dust pollution emissions, respectively. The correlation between PM2.5 and PM10 was strong (R = 0.98). In addition, PM only showed a significant negative correlation with O3. On the contrary, PM showed a significant positive correlation with other gaseous pollutants (SO2, NO2, CO). O3 is only negatively correlated with relative humidity and atmospheric pressure. These findings provide an accurate and effective countermeasure for the coordinated management of air pollution in Cheng-Yu region and the formulation of the regional carbon peaking roadmap. Furthermore, it can improve the prediction accuracy of air pollution under multi-scale meteorological factors, promote effective emission reduction paths and policies in the region, and provide references for related epidemiological research.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11270-023-06279-8.

Associations between air pollutants and hospital admissions for chronic obstructive pulmonary disease in Jinan: potential benefits from air quality improvements

Evidence between air pollution and chronic obstructive pulmonary disease (COPD) is inconsistent and limited in China. In this study, we aim to examine the associations between air pollutants and hospital admissions for COPD, hoping to provide practical advice for prevention and control of COPD. Hospital admissions for COPD were collected from a Grade-A tertiary hospital in Jinan from 2014 to 2020. A generalized additive model (GAM) was used to examine the associations between air pollutants and hospital admissions for COPD. Stratified analysis was also conducted for gender, age (20-74 and ≥75 years), and season (warm and cold). The avoidable number of COPD hospital admissions was calculated when air pollutants were controlled under national and WHO standards. Over the study period, a total of 4,012 hospital admissions for COPD were recorded. The daily hospital admissions of COPD increased by 2.36% (95%CI: 0.13-4.65%) and 2.39% (95%CI: 0.19-4.65%) for per 10 μg/m³ increase of NO₂ and SO₂ concentrations at lag2, respectively. There was no statistically significant difference in health effects caused by increased concentrations of PM_2.5, PM₁₀, CO, and O₃. The health effects of increased SO₂ concentration were stronger in women, the ≥75 years old people and the cold season. About 2 (95%CI: 0-3), 64 (95%CI: 4-132) and 86 (95%CI: 6-177) COPD admissions would be avoided when the SO₂ concentration was controlled below the NAAQS-II (150 μg/m³), NAAQS-I (50 μg/m³), and WHO's AQG2021 standard (40 μg/m³), respectively. These findings suggest that short-term exposure to NO₂ and SO₂ was associated with increased risks of daily COPD admissions, especially for females and the elderly. The control of SO₂ and NO₂ under the national and WHO standards could avoid more COPD admissions and obtain greater health benefits.

Spatiotemporal Evolution and Driving Forces of PM2.5 in Urban Agglomerations in China

With the rapid development of China's economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM_2.5 concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM_2.5 were analyzed in UAs. The effects of natural and social factors on PM_2.5 concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM_2.5 was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM_2.5 in October. (2) The highest PM_2.5 concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM_2.5. Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM_2.5 concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM_2.5 concentration. However, PM_2.5 in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation.

Drivers of PM2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China

Fine particulate matter (PM_2.5) is a major source of air pollution in China. Although there have been many studies of the drivers of PM_2.5 pollution in the megacities clustered in eastern China, the behavior of PM_2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015-2019 obtained from the national air environmental monitoring network to examine variation in PM_2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM_2.5 in the study region. The annual average PM_2.5 concentrations over the study period was 54.3 μg/m³, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320-370 d and 150-200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO₂, NO₂, relative humidity, sunshine duration and temperature. The two most primary variables, NO₂ and SO₂, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO₂ and other factors had the strongest effect on PM_2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM_2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.

The association of meteorological parameters and AirQ+ health risk assessment of PM2.5 in Ratchaburi province, Thailand

Air quality is heavily influenced by rising pollution distribution levels which are a consequence of many artificial activities from numerous sources. This study aims to determine the relationship between meteorological data and air pollutants. The health effects of long-term PM_2.5 were estimated on expected life remaining (ELR) and years of life lost (YLL) indices in Ratchaburi province during the years 2015–2019 using AirQ+ software. Values obtained from the PM_2.5 averaging, and YLL data were processed for the whole population in the age range of 0–29, 30–60 and over 60. These values were entered into AirQ+ software. The mean annual concentration of PM_2.5 was highly variable, with the highest concentration being 136.42 μg/m³ and the lowest being 2.33 μg/m³. The results estimated that the highest and lowest YLL in the next 10 years for all age groups would be 24,970.60 and 11,484.50 in 2017 and 2019, respectively. The number of deaths due to COPD, IHD, and stroke related to long-term exposure to ambient PM_2.5 were 125, 27 and 26, respectively. The results showed that older people (> 64) had a higher YLL index than the groups aged under 64 years. The highest and lowest values for all ages were 307.15 (2015) and 159 (2017). Thus, this study demonstrated that the PM_2.5 effect to all age groups, especially the the elderly people, which the policy level should be awared and fomulated the stratergies to protecting the sensitive group.

The role of infrastructure, socio-economic development, and food security to mitigate the loss of natural disasters

This study shows the impact of risk (hazard, exposure, and vulnerability) and resilience (infrastructure, information and communication technology, institutional quality, food security, women empowerment, economic performance, human capital, emergency workforce, and social capital) indicators on losses due to natural disasters in 24 high-income, 24 upper-middle-income, 30 lower-middle-income, and 12 low-income countries from 1995 to 2019. It develops a new disaster risk index and disaster resilience index using standard index-making procedure (indicators selection, winsorization, normalization, aggregation). The generalized additive modeling was used to explore the non-linear relationship between response and explanatory variables. There exists a positive link between damage due to natural disasters and hazard index (all panels) and exposure index in high-income countries. The decrease in damage due to natural disasters was observed due to an increase in infrastructure (upper-middle-, lower-middle-, and low-income countries), information and communication technology (high-income countries), institutional quality (high-income countries), food security (high- and upper-middle-income countries), women empowerment (lower-middle-income countries), economic performance (high- and low-income countries), human capital (low-income countries), and emergency workforce (upper-middle and lower-middle-income countries). The governments should enhance disaster resilience through Sendai Framework, having seven targets and four priority areas to increase disaster resilience.

Potential years of life lost due to PM2.5-bound toxic metal exposure: Spatial patterns across 60 cities in China

To clarify the spatial patterns of disease burden caused by toxic metals in fine particulate matter (PM_2.5) across China, annual concentration levels of typical toxic metals in PM_2.5 over 60 cities of China were retrieved. Then, potential years of life lost (PYLL) attributable to toxic metal (As, Cd, Cr (VI), Mn, and Ni) exposure was calculated from health risk assessments and lifetable estimates. The results show that Cr(VI) and As were the most polluted metals and greatly exceeded the recommended annual values in the National Ambient Air Quality Standard of China. PYLL for each death (mean ± standard deviation) of 19.8 ± 4.5 years was observed for lung cancer, followed closely by COPD and pneumonia. Furthermore, the PYLL rate (years per 100,000 people) attributable to exposure to these toxic metals was 457 (male: 505, female: 402) years for different cities; therein, Cr(VI) contributed the highest PYLL among these toxic metals, with a proportion of 72.7% (male: 75.3%, female: 69.5%), followed by As of 16.4% (male: 13.8%, female: 19.8%). The concentration level and PYLL both showed large spatial variability, of which the top-ranking cities were observed to be affected by well-developed metal-related industries and coal-powered industrial sectors.

The effect and prediction of diurnal temperature range in high altitude area on outpatient and emergency room admissions for cardiovascular diseases

PURPOSE

Diurnal temperature range (DTR) is a meteorological indicator closely associated with global climate change. Thus, we aim to explore the effects of DTR on the outpatient and emergency room (O&ER) admissions for cardiovascular diseases (CVDs), and related predictive research.

METHODS

The O&ER admissions data for CVDs from three general hospitals in Jinchang of Gansu Province were collected from 2013 to 2016. A generalized additive model (GAM) with Poisson regression was employed to analyze the effect of DTR on the O&ER admissions for all cardiovascular diseases, hypertension, ischemic heart disease (IHD) and stoke. GAM was also used to preform predictive research of the effect of DTR on the O&ER admissions for CVDs.

RESULTS

There were similar positive linear relationships between DTR and the O&ER visits with the four cardiovascular diseases. And the cumulative lag effects were higher than the single lag effects. A 1 °C increase in DTR corresponded to a 1.30% (0.99-1.62%) increase in O&ER admissions for all cardiovascular diseases. Males and elderly were more sensitivity to DTR. The estimates in non-heating season were higher than in heating season. The trial prediction accuracy rate of CVDs based on DTR was between 59.32 and 74.40%.

CONCLUSIONS

DTR has significantly positive association with O&ER admissions for CVDs, which can be used as a prediction index of the admissions of O&ER with CVDs.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

The short-term association of ambient fine particulate air pollution with hypertension clinic visits: A multi-community study in Guangzhou, China

BACKGROUND

The association of ambient fine particulate pollution with daily outpatient clinic visits (OCV) for hypertension in China remains to be investigated.

OBJECTIVES

This study aimed to examine short-term impacts of exposure to fine particulate matter of aerodynamic diameter < 2.5μm (PM_2.5) on daily OCV for hypertension, using a large-scale multi-center community database in Guangzhou, one of the most densely-populated cities in Southern China.

METHODS

We collected a total of 28,548 individual records of OCV from 22 community healthcare facilities in Guangzhou from January 1st to May 7th 2020. Hourly data on air pollutants and daily information on meteorological factors were obtained. According to the World Health Organization air-quality guidelines, daily excessive concentration hours (DECH) was calculated. PM_2.5 daily mean, hourly-peak concentration and DECH were used as the exposure variables. Based on a case-time-control design, the Cox regression model was applied to evaluate the short-term relative risks (RR) of daily OCV for hypertension. Sensitivity analyses were conducted, with nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone being adjusted.

RESULTS

Daily mean and hourly-peak of PM_2.5 were significantly associated with daily OCV for hypertension, while weaker associations were observed for DECH. The estimated RRs at lag day 0 were 1.039 (95% confidence interval [CI]: 1.037, 1.040), 1.851 (95%CI: 1.814, 1.888), and 1.287 (95%CI: 1.276, 1.298), respectively, in association with a 1-unit increase in DECH, daily mean, and hourly-peak concentration of PM_2.5. For the lagged effect, lag₄ models estimated the greatest RRs for PM_2.5 DECH and hourly-peak, whereas a lag₂ model produced the highest for PM_2.5 daily mean.

DISCUSSION

This study consolidates the evidence for a positive correlation between ambient PM_2.5 exposure and risks of hypertensive OCV. It also provides profound insight regarding planning for health services needs and establishing early environmental responses to the worsening air pollution in the communities.

Association between PM10 and specific circulatory system diseases in China

Particulate matter (PM) has been proved to be a risk factor for the development of circulatory system diseases (CSDs) around the world. In this study, we collected daily air pollutants, emergency room (ER) visits for CSDs, and meteorological data from 2009 to 2012 in Beijing, China. After controlling for the long-term trend and eliminating the influence of confounding factors, the generalized additive model (GAM) was used to evaluate the short-term effects of PM10 on CSDs and cause-specific diseases. The results showed that for every 10 μg/m3 increase in PM10, the largest effect estimates in ER visits of total CSDs, arrhythmia, cerebrovascular diseases, high blood pressure, ischemic heart disease and other related diseases were 0.14% (95% CI: 0.06-0.23%), 0.37% (95% CI: - 0.23 to 0.97%), 0.20% (95% CI: 0.00-0.40%), 0.15% (95% CI: 0.02-0.27%), 0.18% (95% CI: 0.02-0.35%) and 0.35% (95% CI: - 0.04 to 0.79%), respectively. When NO2 or SO2 was added into the model, the effect estimates of PM10 were mostly attenuated, while in those models with PM2.5 added, the effect estimates of PM10 were mostly increased. Stratified analysis indicated that PM10 had a greater effect on males and the elderly.

Air pollution and cause-specific mortality: A comparative study of urban and rural areas in China

Air pollution increases the risks of all-cause mortality, cardiovascular mortality and respiratory mortality across China. However, the urban-rural differences in the associations between air pollution and mortality have not been clearly identified. In this study, a distributed lag nonlinear model was used to examine whether the air pollutants-mortality associations vary between urban and rural areas. Then, we used logistic regression analyses to evaluate the air pollutants-mortality relations. Also, generalized additive models were simulated to evaluate the nonlinear curves. Our results showed that the relative risks of air pollution-related mortality were generally higher in rural areas, where PM_2.5 pollution was the dominant factor (p-value < 0.05). Mortality risks for all-cause, cardiovascular and respiratory will increase when average annual PM_2.5 concentrations exceed approximately 38 μg/m³, 41 μg/m³ and 41 μg/m³, respectively, all of which exceed the annual Grade II standards. In urban areas, PM_10-2.5 and NO₂ were associated with mortality (p-value < 0.05). We proposed some area-specific strategies for controlling the NO₂ pollution and PM_10-2.5 pollution in urban areas and the PM_2.5 pollution in rural areas to eliminate the gaps. Our findings identify that rural residents are more sensitive to air pollution than urban residents in China, and this result challenges previous assumptions about the more adverse effects of urbanization on residents' health in developing countries.

Effects of long-term exposure to PM2.5 on years of life lost and expected life remaining in Ahvaz city, Iran (2008-2017)

Ambient air pollution is one of the most significant environmental problems, and many individuals around the world die each year prematurely from diseases caused by this type of pollution. PM_2.5 can transpire deep to the lungs and induce some dangerous health effects in humans. In this study, the health effects of long-term PM_2.5 were estimated on expected life remaining (ELR) and years of life lost (YLL) indices in Ahvaz city during the years 2008-2017 using the AirQ+ software developed by WHO. Values obtained from the PM_2.5 averaging, ELR, and YLL data were processed for the whole population in the age range of 0-64 and over 64. These values were entered into AirQ+ software. The mean annual concentration of PM_2.5 was highly variable, with the highest concentration being 70.72 μg/m³ in 2010 and the lowest 41.97 μg/m³ in 2014. In all studied years, the concentration of PM_2.5 with the variations between 4.2 to 7.07 times was higher than the WHO standard (10 μg/m³). Ahvaz city also did not experience any clean day during the 10-year period, and in 2010, there were 47 very unhealthy days and 27 dangerous days, i.e., the highest number of very unhealthy and dangerous days during the period. The results estimated that the highest and lowest YLL in the next 10 years for all ages groups would be 137,760.49 (2010) and 5035.52 (2014), respectively. Also, the ELR index was lower than the Iranian standard and EPA which was significantly correlated with the concentration of PM_2.5.

Source and exposure apportionments of ambient PM2.5 under different synoptic patterns in the Pearl River Delta region

PM_2.5 is one of the most notorious ambient pollutants in the Pearl River Delta (PRD) region during episodic conditions. In this work, the Comprehensive Air Quality Model with extension (CAMx) was used together with the Particulate Source Apportionment Technology (PSAT) module to analyze the influences of different sources on PM_2.5 concentration in the PRD region under different synoptic patterns (sea high pressure, sub-tropical high pressure and equalizing pressure field). The result shows that the PM_2.5 concentration increases to different degrees under the three synoptic patterns. The emissions outside the PRD region contribute more than 54% under episodic conditions. The source category contribution varies little under different synoptic patterns. Area (46%), mobile (21%) and industry point source (16%) are the major contributors over the three episodic cases. The regional source contributions (from other cities within the PRD) to Foshan, Zhongshan and Zhaoqing are larger and can reach up to 33%. People living in the PRD region are more exposed to pollutants produced from the area and mobile sources. About 80% of the population is exposed to PM_2.5 levels exceeding the IT-3 standard during the pollution episodes.

The Relationship between Daily Concentration of Fine Particulate Matter in Ambient Air and Exacerbation of Respiratory Diseases in Silesian Agglomeration, Poland

The relationship between the worsening of air quality during the colder season of the year and respiratory health problems among the exposed population in many countries located in cold climates has been well documented in numerous studies. Silesian Voivodeship, a region located in southern Poland, is one of the most polluted regions in Europe. The aim of this study was to assess the relationship between daily concentration of particulate matter (PM: PM_2.5 and PM₁₀) in ambient air and exacerbations of respiratory diseases during the period from 1 January 2016 to 31 August 2017 in the central agglomeration area of Silesian Voivodeship. The study results confirmed a significant increase of daily fine particulate matter concentration in ambient air during the cold season in Silesian Voivodeship with a simultaneous increase of the number of outpatient visits and hospitalizations due to respiratory diseases. The moving average concentration was better suited for the modelling of biological response as a result of PM_2.5 or PM₁₀ exposure than the temporal lag of health effects. Each increase of dose expressed in the form of moving average concentration over a longer time leads to an increase in the daily number of respiratory effects. The highest risk of hospitalization due to respiratory diseases was related to longer exposure of PM expressed by two to four weeks of exposure; outpatient visits was related to a shorter exposure duration of 3 days.