Effect of meteorological factors and Air Quality Index on the COVID-19 epidemiological characteristics: an ecological study among 210 countries

Associations between long-term exposure to air pollution, diabetes, and hypertension in metropolitan Iran: an ecologic study

Epidemiological studies on air pollution, diabetes, and hypertension conflict. This study examined air pollution, diabetes, and hypertension in adults in 11 metropolitan areas of Iran (2012-2016). Local environment departments and the Tehran Air Quality Control Company provided air quality data. The VIZIT website and Stepwise Approach to Chronic Disease Risk Factor Surveillance study delivered chronic disease data. Multiple logistic regression and generalized estimating equations evaluated air pollution-related diabetes and hypertension. In Isfahan, Ahvaz, and Tehran, PM2.5 was linked to diabetes. In all cities except Urmia, Yasuj, and Yazd, PM2.5 was statistically related to hypertension. O3 was connected to hypertension in Ahvaz, Tehran, and Shiraz, whereas NO2 was not. BMI and gender predict hypertension and diabetes. Diabetes, SBP, and total cholesterol were correlated. Iran's largest cities' poor air quality may promote diabetes and hypertension. PM2.5 impacts many cities' outcomes. Therefore, politicians and specialists have to control air pollution.

Examining the relationship between atmospheric pollutants and meteorological factors in Asansol city, West Bengal, India, using statistical modelling

Meteorological conditions significantly impact ambient air quality in urban environments. This study focuses on Asansol, known as the "Coal City" and the "Industrial Heart of West Bengal," a notable hotspot for air pollution. Despite its significance, limited research has addressed the influence of meteorological factors on key air pollutants in this urban area. From January 2019 to December 2023, this investigation explores the relationships between meteorological parameters (including atmospheric temperature, relative humidity, rainfall, wind speed) and the concentrations of crucial air pollutants (PM2.5, PM10, NO2, SO2). Temporal trends in air pollutant concentrations are also analysed. The Spearman correlation method is used to establish associations between pollutant concentrations and meteorological variables, while multiple linear regression (MLR) models are employed to assess meteorological factors and potential impact on pollutant concentrations. The analysis reveals a decreasing trend in pollutant concentrations in Asansol. Temperature exhibits negative correlations with all pollutants in all seasons except for a positive correlation during the monsoon. Rainfall consistently displays significant negative correlations with pollutants in all seasons. Relative humidity is negatively correlated with pollutants in all seasons, and wind speed, except during the post-monsoon season, shows negative correlations with all pollutants. Linear models excel in predicting particulate matter concentrations but perform poorly in predicting gaseous contaminants. Accounting for seasonal fluctuations and meteorological parameters, this research enhances the accuracy of air pollution forecasting, contributing to a better understanding of air quality dynamics in Asansol and similar urban areas.

Short-term effect of meteorological factors on COVID-19 mortality in Qom, Iran

The present study was conducted to assess the short-term effects of the meteorological factors on the COVID-19 mortality in Qom, Iran. The GAM with a quasi-Poisson link function was used to evaluate the impact of temperature, DTR, relative humidity, and absolute humidity on the COVID-19 mortality, controlling potential confounders such as time trend, air pollutants, and day of the week. The results showed that the risk of COVID-19 mortality was reduced, in single-day lag/multiple-day average lag, per one-unit increase in absolute humidity (percentage change in lag 0=-33.64% (95% CI (-42.44, -23.49)), and relative humidity (percentage change in lag 0=-1.87% (95% CI (-2.52, -1.22)). Also, per one-unit increase in DTR value, COVID death risk increased in single-day and multiple-day average lag. This study demonstrated a significant relationship between the four meteorological variables and the COVID-19 mortality.

Association between meteorological factors and varicella incidence: a multicity study in Yunnan Province, China

This multicenter study aimed to investigate the relationship between varicella incidence and meteorological factors including mean temperature, relative humidity, sunshine duration, diurnal temperature difference, wind speed, and rainfall, as previous studies have produced varying results. Our study also sought to identify potential sources of heterogeneity. Data on reported daily varicella numbers and meteorological factors were collected for 14 cities in Yunnan Province from 2017 to 2021. A distribution-lagged nonlinear model was constructed to explore the relationship between meteorological conditions and varicella incidence in each included city. We then used multiple meta-regression to explore sources of heterogeneity using demographic economics indicators, air pollutants, and geographic location as potential modifiers. The cumulative hazard effect plot showed an inverted S-shape for the relationship between temperature and varicella, with the smallest RR (relative risk) (0.533, 95% CI: 0.401-0.708) at temperatures up to 27.2 °C. The maximum RR (1.171, 95% CI: 1.001-1.371) was obtained when the relative humidity was equal to 98.5%. The RR (1.164, 95% CI: 1.002-1.352) was greatest at a diurnal temperature range of 2 °C (1.164, 95% CI: 1.002-1.352) and least (0.913, 95% CI: 0.834-0.999) at a diurnal temperature range of 16.1 °C. The maximum RR (1.214, 95% CI: 1.089-1.354) was obtained at 0 h of sunshine, and the minimum RR (0.808, 95% CI: 0.675-0.968) was obtained at 12.4 h of sunshine. The RR (0.792, 95% CI: 0.633-0.992) was minimum at a wind velocity of 4.8 m/s. Residual heterogeneity ranged from 1 to 42.7%, with PM10 (particles with an aerodynamic diameter less than 10 μm), GDP (gross domestic product), and population density explaining some of this heterogeneity. The temperature has a dual effect on varicella incidence. Varicella cases are negatively correlated with diurnal temperature range, sunshine duration, and wind speed, and positively correlated with relative humidity. GDP and PM10 may have a significant role in altering the association between temperature and varicella, while PM10 and population density may alter the association between wind velocity and varicella.

Using a time-varying LCAR-based strategy to investigate the spatiotemporal pattern of association between short-term exposure to particulate matter and COVID-19 incidence: a case study in the continental USA

Numerous studies have demonstrated that short-term exposure to particulate matter less than 10 μm (PM10) is positively associated with the COVID-19 incidence. However, no study has investigated the spatiotemporal pattern in this association, which plays important roles in identifying high-susceptibility regions and stages of epidemic. In this work, taking the 49 native states in America as an example, we used an advanced strategy to investigate this issue. First, time-series generalized additive model (GAM) were independently constructed to obtain the state-specific associations between short-term exposure to PM10 and the daily COVID-19 cases from 1 April 2020 to 31 December 2021. Then, a Leroux-prior-based conditional autoregression (LCAR) was used to spatially smoothen the associations. Third, the temporal variation of association and the reasons underlying the spatiotemporal heterogeneity were investigated by incorporating the time-varying GAM into LCAR. Results showed that PM10 was adversely associated with COVID-19 incidence in all the states. On average, a 10 μg/m3 increase of PM10 was associated with a 7.38% (95% CI 5.20-9.64%) increase in COVID-19 cases. A substantial spatial heterogeneity was observed, with strong associations in the middle and northeastern regions and weak associations in the western regions. The temporal trend of association presented a U shape, with the strongest association in the end of 2021. The vaccination rate was examined as a significant effect modifier. Our study provided the first evidence about the spatiotemporal pattern in PM10-COVID-19 associations and suggested that air pollution deserves more attention in the post-pandemic era and in the middle and northeastern regions in America for COVID-19 control and prevention.

Environmental factors and incidence of thyroid cancer in the world (1990-2019): an ecological study

Thyroid cancer (TC) is the most common endocrine malignancy. Environmental risk factors such as presence of pollutants in air as well as the combustion of fossil fuels or carbon as a cooking habit in closed environments inside houses affect thyroid hormonal homeostasis and diseases. This study aimed to estimate the association between environmental risk factors and the incidence of thyroid cancer worldwide from 1990 to 2019 including particulate pollutants coming from fossil fuels employed in closed environments. Data on the incidence of thyroid cancer and some environmental risk factors were extracted from the Global Cancer Observatory (GLOBOCAN) for 204 countries and territories from 1990 to 2019. Pearson's correlation coefficient was used to determine the correlation between the thyroid cancer incidence and environmental risk factors. Finally, a generalized additive model was fitted for modeling. R 3.5.0 was used for analysis of the data. The most relevant results showed that the age-standardized incidence rate (ASIR) of thyroid cancer has a positive and significant correlation with environmental air pollution by O3 (r=0.63, P value<0.001), by particulate matter pollution (r=0.23, P value<0.001), and by household PM2.5 air pollution (r=0.52, P value≤0.001). In contrast, the correlation between ASIR and high temperature (T>25.6°C) (r=-0.27, P value<0.001) is negative and significant. The modeling results showed that particulate matter pollution and O3 pollution and household PM2.5 air pollution which originated from solid fuels are risk factors for thyroid cancer. Therefore, more research in this field is necessary in areas with high levels of air pollution at the national and international levels.

Ecological study on household air pollution exposure and prevalent chronic disease in the elderly

Older people spend most of their time indoors. Limited evidence demonstrates that exposure to indoor air pollutants might be related to chronic complications. This study aimed to estimate the correlation between household air pollution (HAP)'s long-term exposure and the prevalence of elevated hypertension, diabetes mellitus (DM), obesity, and low-density lipoprotein (LDL) cholesterol. From the Global Burden disease dataset, we extracted HAP, hypertension, DM, body mass index, and LDL cholesterol data from Iran from 1990 to 2019 to males and females in people over 50 years. We present APC and AAPC and their confidence intervals using Joinpoint Software statistical software. R software examined the correlation between HAP and hypertension, DM2, Obesity, and high LDL cholesterol. Our finding showed a significant and positive correlation between HAP exposure and prevalence of high low-density lipoprotein cholesterol (p ≤ 0.001, r = 0.70), high systolic blood pressure (p ≤ 0.001, r = 0.63), and high body mass index (p ≤ 0.001, r = 0.57), and DM2 (p ≤ 0.001, r = 0.38). The analysis results also illustrated a positive correlation between indoor air pollution and smoking (p ≤ 0.001, r = 0.92). HAP exposure might be a risk factor for elevated blood pressure, DM, obesity, and LDL cholesterol and, consequently, more serious health problems. According to our results, smoking is one of the sources of HAP. However, ecological studies cannot fully support causal relationships, and this article deals only with Iran. Our findings should be corroborated in personal exposure and biomonitoring approach studies.

Analysis of the distribution characteristics of prostate cancer and its environmental factors in China

The high incidence and mortality and the increasing trend of prostate cancer has been one of the public health issues in many countries and regions. Meanwhile, the spatio-temporal heterogeneity of prostate cancer implies that lifestyle and ecological changes may be associated with prostate cancer, however, sufficient evidence is still lacking. This paper tried to reveal the spatial and temporal distribution characteristics of prostate cancer in China and explore the potential associations with related socioeconomic and natural condition factors. Data on prostate cancer incidence and mortality in 182 counties (districts) in mainland China from 2014-2016 were collected, and the distribution characteristics of prostate cancer were analyzed using spatiotemporal scan statistic. Spatial regression models and geodetector method were used to analyze the potential associations between meteorological conditions, socioeconomic development, and prostate cancer incidence and mortality. SaTScan, GeoDa, and GeoDetector were used for the above statistical analyses. The high-risk clusters for prostate cancer incidence and mortality were located in southeastern China, and the low-risk clusters were located in north-central China. Spatial regression models showed that the number of industrial enterprises/km² (incidence: β = 0.322, P < 0.001; mortality: β = 0.179, P < 0.001), GDP (incidence:β = 0.553, P < 0.001; mortality: β = 0.324, P < 0.001), number of beds in medical and health institutions/1000 persons (incidence: β = 0.111, P = 0.005; mortality: β = 0.068, P = 0.021), and urbanization rate (incidence: β = 0.156, P < 0.001; mortality: β = 0.100, P < 0.001) were positively associated with the incidence and mortality of prostate cancer. The urbanization rate (incidence: q = 0.185, P < 0.001; mortality: q = 0.182, P < 0.001) has the greatest explanatory power, and the interaction of all factors was bivariate enhanced or nonlinearly enhanced. The distribution of prostate cancer in China has obvious spatial heterogeneity. The incidence and mortality rate of prostate cancer are on the rise, and special plans should be formulated in each region according to local conditions.

Is there a casual relation between air pollution and dementia?

Dementia has been cited as a critical public health risk in the contemporary world, while few empirical researchers try to reveal the casual relationship between air pollutant concentrations (APCs) and dementia, especially given the increasing prevalence of air pollution on a global scale. Accordingly, this paper tries to infer the causal relationship between APCs and dementia. The 59,605 valid data was compiled through a combination of the statistic from the China Family Panel Study, China Environmental Statistics Yearbook, World Meteorological Association and China National Bureau of Statistics. The RD design of this study utilizes the discontinuous variation in APCs and dementia as one crosses the Huai River boundary, which is an arbitrary heating policy that causes the significant difference in APCs between the north and south of China. We used stata17.0 to analyze the data. The results of the RD regression indicated that a 100 μ g/m³ rise in APCs led to an increase of 42.4% in the hazard ratio of suffering dementia (Coeff=-0.58, SD= 0.23, P < 0.05). Meanwhile, heterogeneous models revealed that the hazard ratio of suffering dementia by APCs was more significant in the older compared to younger (coeff= 1.35 vs coeff= 1.55, P < 0.05), male compared to female (coeff= 1.62 vs coeff= 0.71, P < 0.05), smoking compared to non-smoke (coeff= 2.12 vs coeff= 0.93, P < 0.05), and thin groups compared to medium and obesity (coeff= 2.05 vs coeff= 1.22, coeff= 1.28, P < 0.05). In addition, the O3 and SO2 were the air pollutants with the highest (coeff= 1.54, P < 0.05) and lowest effects (coeff= 0.81, P < 0.05) on the hazard ratio of suffering dementia among the five APCs, respectively. And the robustness of the results was ensured by changing the RD bandwidth, polynomial order. The results indicated that APCs significantly induced the hazard ratio of suffering dementia of Chinese residents, which provides empirical evidence in supporting the Chinese government to invest more in combating air pollution and ensure the public health of Chinese residents.

Attributable risk and economic burden of pneumonia among older adults admitted to hospital due to short-term exposure to airborne particulate matter: a time-stratified case-crossover study from China

Many studies have proven the relationship between air pollutants and respiratory diseases, but few studies have assessed the impacts of air particulate matter exposure on older patients with pneumonia. This study aimed to reveal the impacts of short-term exposure to air particulate matter on the daily number of older adult patients hospitalized due to pneumonia and calculate the economic costs attributable to this exposure. We collected inpatient data from 9 city hospitals in Sichuan Province, China, from January 1, 2018, to December 31, 2019, and calculated odds ratios and 95% confidence intervals using a time-stratified case-crossover study design and an attributable risk model to calculate the economic burden due to particulate matter pollution. It was found that for every 10 μg/m³ increase in PM_2.5 and PM₁₀ concentrations, the daily number of older adult pneumonia inpatients increased by 1.5% (95% CI: 1.010-1.021) and 1.0% (95% CI: 1.006-1.014), respectively. Those 65 ~ 79 years old were more susceptible to air particulate pollutants (P < 0.05). During the study period, the total hospitalization costs and out-of-pocket expenses attributable to PM_2.5 and PM₁₀ exposure were 44.60 million CNY (6.22%) and 16.03 million CNY (6.21%), respectively, with PM_2.5 being the primary influencing factor. This study revealed the relationship between particulate matter pollution and pneumonia among older adults. The role of policies to limit particulate matter concentrations in reducing disease burden among older adults can be further explored.

The relationship among air pollution, meteorological factors and COVID-19 in the Brussels Capital Region

In great metropoles, there is a need for a better understanding of the spread of COVID-19 in an outdoor context with environmental parameters. Many studies on this topic have been carried out worldwide. However, there is conflicting evidence regarding the influence of environmental variables on the transmission, hospitalizations and deaths from COVID-19, even though there are plausible scientific explanations that support this, especially air quality and meteorological factors. Different urban contexts, methodological approaches and even the limitations of ecological studies are some possible explanations for this issue. That is why methodological experimentations in different regions of the world are important so that scientific knowledge can advance in this aspect. This research analyses the relationship between air pollution, meteorological factors and COVID-19 in the Brussels Capital Region. We use a data mining approach that is capable of extracting patterns in large databases with diverse taxonomies. Data on air pollution, meteorological, and epidemiological variables were processed in time series for the multivariate analysis and the classification based on association. The environmental variables associated with COVID-19-related deaths, cases and hospitalization were PM_2.5, O₃, NO₂, black carbon, radiation, air pressure, wind speed, dew point, temperature and precipitation. These environmental variables combined with epidemiological factors were able to predict intervals of hospitalization, cases and deaths from COVID-19. These findings confirm the influence of meteorological and air quality variables in the Brussels region on deaths and cases of COVID-19 and can guide public policies and provide useful insights for high-level governmental decision-making concerning COVID-19. However, it is necessary to consider intrinsic elements of this study that may have influenced our results, such as the use of air quality aggregated data, ecological fallacy, focus on acute effects in the time-series study, the underreporting of COVID-19, and the lack of behavioral factors.

Ecological study of ambient air pollution exposure and mortality of cardiovascular diseases in elderly

As an independent risk factor, ambient air pollution can assume a considerable part in mortality and worsening of cardiovascular disease. We sought to investigate the association between long-term exposure to ambient air pollution and cardiovascular disease mortality and their risk factors in Iranian's elderly population. This inquiry was conducted ecologically utilizing recorded data on cardiovascular disease mortality from 1990 to 2019 for males and females aged 50 years or more from the Global Burden of Disease dataset. Data was interned into Joinpoint software 4.9.0.0 to present Annual Percent Change (APC), Average Annual Percent Change (AAPC), and its confidence intervals. The relationship between recorded data on ambient air pollution and cardiovascular disease' mortality, the prevalence of high systolic blood pressure, high LDL cholesterol levels, high body mass index, and diabetes mellitus type2 was investigated using the Spearman correlation test in R 3.5.0 software. Our finding demonstrated that cardiovascular diseases in elderly males and females in Iran had a general decreasing trend (AAPC = -0.77% and -0.65%, respectively). The results showed a positive correlation between exposure to ambient ozone pollution (p ≤ 0.001, r = 0.94) ambient particulate and air pollution (p < 0.001, r = 0.99) and mortality of cardiovascular disease. Also, ambient air pollution was positively correlated with high systolic blood pressure (p < 0.001, r = 0.98), high LDL cholesterol levels (p < 0.001, r = 0.97), high body mass index (p < 0.001, r = 0.91), diabetes mellitus type2 (p < 0.001, r = 0.77). Evidence from this study indicated that ambient air pollution, directly and indirectly, affects cardiovascular disease mortality in two ways by increasing the prevalence of some traditional cardiovascular disease risk factors. Evidence-based clinical and public health methodologies are necessary to decrease the burden of death and disability associated with cardiovascular disease.

Correlating the trends of COVID-19 spread and air quality during lockdowns in Tier-I and Tier-II cities of India-lessons learnt and futuristic strategies

The present study focuses on the impact of early imposed lockdowns and following unlocking phases on the status of air quality in six Tier-I and nine Tier-II cities of India as compared to the pre-lockdown measures. Furthermore, the study highlights the possible correlation of air quality index (AQI) with the initial trend of COVID-19 issues including the vaccination cases. Based on the statistical data analysis, we observed that the long-term averages for representing the short-term pre-lockdown conditions can impose a healing effect to the observed anomalies in air pollution data. However, the reduction in air pollution during the imposed lockdown series was only a phenomenal consequence, and the trends started reversing during the later phases of partial unlocking, where the correlation showed reversing trends. Being a yearly averaged parameter, the marginal reductions in the exceedance factor (EF) alone could not dictate air quality compared to the AQI. As there is incoherent variability in the pollutant distributions among the cities during various phases of the study, the trend analysis served as a preferable criterion to choose the preferred sources of variations. Based on the results, the correlation analysis revealed that air quality expressed in terms of AQI can act as an important precursor to estimate the critical phase of COVID-19 spread and the effectiveness of various control measures taken during each phase. Based on our proposed ranking, Kolkata and Patna are ranked first in the Tier-I and Tier-II cities respectively according to their responsiveness to the various institutionalized restrictions in terms of air quality parameters.

PM2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model

Compiling fine-resolution geospatial PM_2.5 concentrations data is essential for precisely assessing the health risks of PM_2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM_2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM_2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM_2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM_2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran's I statistic (a value of 0.136 with p-value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM_2.5 concentration, which would be beneficial for precise health risk assessment of PM_2.5 pollution exposure.

The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review

Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM_2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM_2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.