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The detrimental effects of PM2.5 on the health of urban populations, especially in developing countries, are being carefully addressed by governments worldwide. To mitigate the adverse health effects of air pollution, this paper presents a case study and suggests a three-phase plan for implementing a prediction and control approach in the Thai metro region of Nakhon Ratchasima (NRM). A data-driven assessment of the region's air pollution situation is conducted in the initial phase, which is then followed by an analysis of the health risks related to PM2.5 concentrations. It is therefore imperative to determine which machine learning model has the best capabilities to estimate PM2.5 concentration. With the help of this advanced predictive capability, decision-makers will have an important window of opportunity to put preventive measures in place to safeguard the population from the adverse effects of air pollution. The study showed that there were respiratory diseases in the NRM area because of high concentrations of PM2.5 particles, primarily from vehicle exhaust, which far exceeded Thailand's threshold of 15 μg m−3. A total of 10,379 people, or 16.91% of the affected population, lived in zones with PM2.5 concentrations >38 μg m−3 in 2019. Another 741 people, or 1.21% of the population, lived in zones with PM2.5 values between 36 and 37 μg m−3. The study finds that Extra-Trees regression combined with AdaBoost has the best-predicting capacity for PM2.5 ambient concentration in the NRM area out of the 13 machine-learning regression models examined. This case study presents an additional tool for policymakers to employ in controlling adverse health effects in urban areas.

Exposure to ambient ozone (O3) O3 is associated with impacts on human health. O3 is a secondary pollutant whose concentrations are determined inter alia by emissions of precursors such as oxides of nitrogen (NOx) and volatile organic compounds (VOCs), and thus future health burdens depend on policies relating to climate and air quality. While emission controls are expected to reduce levels of PM2.5 and NO2 and their associated mortality burdens, for secondary pollutants like O3 the picture is less clear. Detailed assessments are necessary to provide quantitative estimates of future impacts to support decision-makers. We simulate future O3 across the UK using a high spatial resolution atmospheric chemistry model with current UK and European policy projections for 2030, 2040 and 2050, and use UK regional population-weighting and latest recommendations on health impact assessment to quantify respiratory emergency hospital admissions associated with short-term effects of O3. We estimate 60,488 admissions in 2018, increasing by 4.2%, 4.5% and 4.6% by 2030, 2040 and 2050 respectively (assuming a fixed population). Including future population growth, estimated emergency respiratory hospital admissions are 8.3%, 10.3% and 11.7% higher by 2030, 2040 and 2050 respectively. Increasing O3 concentrations in future are driven by reduced nitric oxide (NO) in urban areas due to reduced emissions, with increases in O3 mainly occurring in areas with lowest O3 concentrations currently. Meteorology influences episodes of O3 on a day-to-day basis, although a sensitivity study indicates that annual totals of hospital admissions are only slightly impacted by meteorological year. While reducing emissions results in overall benefits to population health (through reduced mortality due to long-term exposure to PM2.5 and NO2), due to the complex chemistry, as NO emissions reduce there are associated local increases in O3 close to population centres that may increase harms to health.

Although Ahvaz is considered as one of the warmest cities around the world, few epidemiological studies have been conducted on the adverse effects of temperature on human health using thermal indices in this city. This study investigates the relation between physiologically equivalent temperature (PET) and respiratory hospital admissions in Ahvaz. Distributed lag non-linear models (DLNMs) combined with quasi-Poisson regression models were used to investigate the relation between PET and respiratory disease hospital admissions, adjusted for the effect of time trend, air pollutants (NO2, SO2, and PM10), and weekdays. The analysis was performed by utilizing R software. Low PET values significantly decreased the risk of hospital admissions for total respiratory diseases, respiratory diseases in men and women, chronic obstructive pulmonary diseases (COPD), and bronchiectasis. However, low PET (16.9°C) in all lags except lag 0–30 significantly increased the risk of hospital admissions for asthma. The results indicate that in Ahvaz, which has a warm climate, cold weather decreased overall respiratory hospital admissions, except for asthma. [ABSTRACT FROM AUTHOR]

Air pollution is likely to have adverse effects on human health. This study was conducted to determine the effect of air pollution on respiratory disease hospital admission in Ahvaz, one of the most polluted cities in the Middle East. Daily information about respiratory disease admissions and air pollutants during 2008–2018 were inquired. Adjusted Quasi-Poisson regression combined with linear distributed lag models were used. There was a significant relation between increased O 3 and respiratory hospital admissions for 65–74 year olds, ≥75 year olds, and pyothorax-abscesses of the lung and mediastinum; between increased NO and respiratory hospital admissions for ≥75 year olds, chronic lower respiratory diseases, diseases of upper respiratory tract and respiratory diseases principally affecting the interstitium; between increased NO 2 and respiratory hospital admissions for ≥75 year olds; between increased CO and respiratory hospital admissions and between increased SO 2 and respiratory hospital admissions for ≥75 year olds. The risk of respiratory hospital admissions increased in both males and females for increase in PM 2.5. Ambient O 3 , NO, NO 2 CO and SO 2 can increase admission for respiratory diseases on the same day and at short lags in Ahvaz. This evidence emphasizes the need to implement policies for reducing air pollution. [ABSTRACT FROM AUTHOR]

Many epidemiologic studies concerned with acute exposure to ambient PM 2.5 have reported positive associations for respiratory disease hospitalization. However, few studies have investigated this relationship in Kuwait and extrapolating results from other regions may involve considerable uncertainty due to variations in concentration levels, particle sources and composition, and population characteristics. Local studies can provide evidence for strategies to reduce risks from episodic exposures to high levels of ambient PM 2.5 and generating hypotheses for evaluating health risks from chronic exposures. Therefore, using speciated PM 2.5 data from local samplers, we analyzed the impact of daily total and source-specific PM 2.5 exposure on respiratory hospitalizations in Kuwait using a case-crossover design with conditional quasi-Poisson regression. Total and source-specific ambient PM 2.5 were modeled using 0-5-day cumulative distributed lags. For total PM 2.5 , we observed a 0.16% (95% confidence interval [CI] = 0.05, 0.27%) increase in risk for respiratory hospitalization per 1 μg/m3 increase in concentration. Of the source factors assessed, dust demonstrated a statistically significant increase in risk (0.16%, 95% CI = 0.04, 0.29%), and the central estimate for regional PM 2.5 was positive (0.11%) but not statistically significant (95% CI = −0.11, 0.33%). No effect was observed from traffic emissions and 'other' source factors. When hospitalizations were stratified by sex, nationality, and age, we found that female, Kuwaiti national, and adult groups had higher effect estimates. These results suggest that exposure to ambient PM 2.5 is harmful in Kuwait and provide some evidence of differential toxicity and effect modification depending on the PM 2.5 source and population affected. • Extrapolating results from other regions involves considerable uncertainty. • Using data collected from local samplers, positive relationships were observed. • Acute exposure to particles increased the risk of respiratory hospitalization. • Some evidence of differential toxicity and effect modification exists. • Controls are necessary to reduce episodic exposures to high levels of particles. [ABSTRACT FROM AUTHOR]

[Display omitted] • Policies on anthropogenic emissions dominate health impacts of air pollution. • O 3 simulated at 3 km resolution for 2030, 2040 and 2050 emissions pathways. • Hospitalisations calculated with latest UK health impact assessment recommendations. • 7078 increase in respiratory hospitalisations by 2050 for current policy c.f. 2018. • Estimated increases are 2–3 times lower if population growth is not included. Exposure to ambient ozone (O 3) O 3 is associated with impacts on human health. O 3 is a secondary pollutant whose concentrations are determined inter alia by emissions of precursors such as oxides of nitrogen (NO x) and volatile organic compounds (VOCs), and thus future health burdens depend on policies relating to climate and air quality. While emission controls are expected to reduce levels of PM 2.5 and NO 2 and their associated mortality burdens, for secondary pollutants like O 3 the picture is less clear. Detailed assessments are necessary to provide quantitative estimates of future impacts to support decision-makers. We simulate future O 3 across the UK using a high spatial resolution atmospheric chemistry model with current UK and European policy projections for 2030, 2040 and 2050, and use UK regional population-weighting and latest recommendations on health impact assessment to quantify respiratory emergency hospital admissions associated with short-term effects of O 3. We estimate 60,488 admissions in 2018, increasing by 4.2%, 4.5% and 4.6% by 2030, 2040 and 2050 respectively (assuming a fixed population). Including future population growth, estimated emergency respiratory hospital admissions are 8.3%, 10.3% and 11.7% higher by 2030, 2040 and 2050 respectively. Increasing O 3 concentrations in future are driven by reduced nitric oxide (NO) in urban areas due to reduced emissions, with increases in O 3 mainly occurring in areas with lowest O 3 concentrations currently. Meteorology influences episodes of O 3 on a day-to-day basis, although a sensitivity study indicates that annual totals of hospital admissions are only slightly impacted by meteorological year. While reducing emissions results in overall benefits to population health (through reduced mortality due to long-term exposure to PM 2.5 and NO 2), due to the complex chemistry, as NO emissions reduce there are associated local increases in O 3 close to population centres that may increase harms to health. [ABSTRACT FROM AUTHOR]

Background: Ambient air pollution, is one of the most frequently stated environmental problems. Many epidemiological studies have documented adverse health effects for ambient air pollution. This study aimed to investigate the association between ambient air pollution and respiratory hospital admissions. Methods: In this ecological time series study data about air pollutant concentrations including CO, NO2, O3, PM2.5, PM10 and SO2 and, respiratory hospital admissions in the urban population of Arak, from January 1st 2010 to December 31st 2015; were inquired, from the Arak Department of Environment, and two major hospitals, respectively. Meteorological data were inquired for the same period as well. Time-series regression analysis with a distributed lag model, controlled for seasonality long-time trends, weather and day of the week, was used for data analysis. Results: Every 10 μg/m³ increase in NO2, and PM10 and every 1 mg/m³ increase in CO at lag 0 corresponded to a RR = 1.032 (95%CI, 1.003-1.06), RR = 1.01 (95%CI, 1.004-1.017) and RR = 1.09 (95%CI, 1.04-1.14), increase in respiratory disease hospitalizations, respectively. Males and the elderly were found to be more susceptible than females and other age groups to air pollutants in regard to respiratory disease admissions. Conclusions: The results of this study showed that outdoor air pollutants significantly increase respiratory hospital admissions; especially among the men and elders in Arak. [ABSTRACT FROM AUTHOR]

The effect of biological (pollen) and chemical air pollutants on respiratory hospital admissions for the Szeged region in Southern Hungary is analysed. A 9-year (1999-2007) database includes-besides daily number of respiratory hospital admissions-daily mean concentrations of CO, PM, NO, NO, O and SO. Two pollen variables ( Ambrosia and total pollen excluding Ambrosia) are also included. The analysis was performed for patients with chronic respiratory complaints (allergic rhinitis or asthma bronchiale) for two age categories (adults and the elderly) of males and females. Factor analysis was performed to clarify the relative importance of the pollutant variables affecting respiratory complaints. Using selected low and high quantiles corresponding to probability distributions of respiratory hospital admissions, averages of two data sets of each air pollutant variable were evaluated. Elements of these data sets were chosen according to whether actual daily patient numbers were below or above their quantile value. A nonparametric regression technique was applied to discriminate between extreme and non-extreme numbers of respiratory admissions using pollen and chemical pollutants as explanatory variables. The strongest correlations between extreme patient numbers and pollutants can be observed during the pollen season of Ambrosia, while the pollen-free period exhibits the weakest relationships. The elderly group with asthma bronchiale is characterised by lower correlations between extreme patient numbers and pollutants compared to adults and allergic rhinitis, respectively. The ratio of the number of correct decisions on the exceedance of a quantile resulted in similar conclusions as those obtained by using multiple correlations. [ABSTRACT FROM AUTHOR]

Abstract: The aim of the study is to analyse the joint effect of biological (pollen) and chemical air pollutants, as well as meteorological variables, on the hospital admissions of respiratory problems for the Szeged region in Southern Hungary. The data set used covers a nine-year period (1999–2007) and is unique in the sense that it includes—besides the daily number of respiratory hospital admissions—not just the hourly mean concentrations of CO, PM10, NO, NO2, O3 and SO2 with meteorological variables (temperature, global solar flux, relative humidity, air pressure and wind speed), but two pollen variables (Ambrosia and total pollen excluding Ambrosia) as well. The analysis was performed using three age categories for the pollen season of Ambrosia and the pollen-free season. Meteorological elements and air pollutants are clustered in order to define optimum environmental conditions of high patient numbers. ANOVA was then used to determine whether cluster-related mean patient numbers differ significantly. Furthermore, two novel procedures are applied here: factor analysis including a special transformation and a time-varying multivariate linear regression that makes it possible to determine the rank of importance of the influencing variables in respiratory hospital admissions, and also compute the relative importance of the parameters affecting respiratory disorders. Both techniques revealed that Ambrosia pollen is an important variable that influences hospital admissions (an increase of 10 pollen grains m−3 can imply an increase of around 24% in patient numbers). The role of chemical and meteorological parameters is also significant, but their weights vary according to the seasons and the methods. Clearer results are obtained for the pollination season of Ambrosia. Here, a 10 μg m−3 increase in O3 implies a patient number response from −17% to +11%. Wind speed is a surprisingly important variable, where a 1 m s−1 rise may result in a hospital admission reduction of up to 42–45%. [Copyright &y& Elsevier]

To examine the impact of bushfire smoke on hospital admission rates for respiratory disease, a time series study was conducted in Brisbane, Australia. Data on particles of 10 microns or less in aerodynamic diameter (PM 10 ) per cubic metre, bushfire events, meteorological conditions, and daily respiratory hospital admissions were obtained for the period of 1 July 1997 to 31 December 2000. A generalized linear model with the negative binomial distribution was used to estimate the effects of bushfire smoke on respiratory hospital admissions. The results of this study show that daily respiratory hospital admission rates consistently increased with increasing levels of PM 10 for both bushfire and non-bushfire periods. This relationship appeared stronger during bushfire periods than non-bushfire periods, especially for the current day. The findings suggest that bushfire smoke was statistically significantly associated with an increased risk of respiratory hospital admissions in Brisbane ( p [ABSTRACT FROM AUTHOR]

Most of the evidence regarding the association between air pollution and health comes from studies in the USA. Nevertheless, air pollution has become a concern also for local authorities of Latin-American cities and Bogotá, Colombia is no exception. This paper will develop a model to define a concentration-response function between three of the most important air pollutants in Bogotá and the daily respiratory hospital admission counts in the city during the year of 1998. This article pretends to further work on this area by giving the first step towards a more detailed estimation of the costs of air pollution in Bogotá. [ABSTRACT FROM AUTHOR]