Daily Air Pollution Research Articles

Exploring Associations Between Short-Term Air Pollution and Daily Outpatient Visits for Allergic Rhinitis.

Many studies have reported that exposure to air pollution increases the likelihood of acquiring allergic rhinitis (AR). This study investigated associations between short-term air pollution exposure and AR outpatient visits. The Department of Otorhinolaryngology, Affiliated Hospital of Hangzhou Normal University provided AR outpatient data from January 1, 2019 to December 31, 2021. Daily air quality information for that period was gathered from the Hangzhou Air Quality Inspection Station. We used the Poisson's generalized additive model (GAM) to investigate relationships between daily outpatient AR visits and air pollution, and investigated lag-exposure relationships across days. Subgroup analyses were performed by age (adult (>18 years) and non-adult (<18 years)) and sex (male and female). We recorded 20,653 instances of AR during the study period. Each 10 g/m3 increase in fine particulate matter (PM10 and PM2.5) and carbon monoxide (CO) concentrations was associated with significant increases in AR outpatient Visits. The relative risks (RR) were: 1.007 (95% confidence interval (CI): 1.001-1.013), 1.026 (95% CI: 1.008-1.413), and 1.019 (95% CI: 1.008-1.047). AR visits were more likely due to elevated PM2.5, PM10, and CO levels. Additionally, children were more affected than adults. To better understand the possible effects of air pollution on AR, short-term exposure to ambient air pollution (PM2.5, PM10, and CO) may be linked to increased daily outpatient AR visits.

A time-series analysis of short-term ambient ozone exposure and hospitalizations from acute myocardial infarction in Henan, China

Epidemiological studies in recent years have identified an association between exposure to air pollutants and acute myocardial infarction (AMI); however, the association between short-term ozone (O3) exposure and AMI hospitalization remains unclear, particularly in developing countries. Therefore, this study collected information on 24,489 AMI patients, including daily air pollutant and meteorological data in Henan, China, between 2016 and 2021. A distributed lagged nonlinear model combined with a Poisson regression model was used to estimate the nonlinear lagged effect of O3 on AMI hospitalizations. We also quantified the effects of O3 on the number of AMI hospitalizations, hospitalization days, and hospitalization costs. The results showed that single- and dual-pollution models of O3 at lag0, lag1, and lag (01–07) were risk factors for AMI hospitalizations, with the most significant effect at lag03 (RR = 1.132, 95% CI:1.083–1.182). Further studies showed that males, younger people (15–64 years), warm seasons, and long sunshine duration were more susceptible to O3. Hospitalizations attributable to O3 during the study period accounted for 11.66% of the total hospitalizations, corresponding to 2856 patients, 33,492 hospital days, and 90 million RMB. Maintaining O3 at 10–130 µg/m3 can prevent hundreds of AMI hospitalizations and save millions of RMB per year in Henan, China. In conclusion, we found that short-term exposure to O3 was significantly associated with an increased risk of hospitalization for AMI in Henan, China, and that further reductions in ambient O3 levels may have substantial health and economic benefits for patients and local healthcare facilities.Graphical

Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP

Air quality level has a complex nonlinear relationship with air pollutant and meteorological conditions, including multiple factors, overlapping information, and difficulty solving equations. In order to identify significant factors, remove correlations, reduce data dimensionality, and simplify the model structure, a BP neural network model for air quality level prediction optimized by stepwise discriminant analysis (STEPDISC) and principal component analysis (PCA) is proposed with 12 factors of historical daily meteorology and air pollutants in Bayannur city as samples. The results showed that, at the significance level of 0.01, the STEPDISC method retained 9 significant impact factors. The PCA method made an orthogonal linear combination of the 9 factors to form the principal components, and the contribution of the top 5 principal components were 37.6%, 19.2%, 15.3%, 8.8%, and 7.7%. At a contribution threshold of 0.85, the top 5 principal component scores were used as input nodes to construct the STEPDISC-PCA-BP model, which had a prediction accuracy of 85.5%.Compared with the PCA-BP and BP model, which had a prediction accuracy of 61.8% and 56.7%, respectively, the STEPDISC-PCA-BP model has a higher prediction accuracy, shorter time, and lower complexity of structure and data dimensionality, and can provide the necessary technical support for the local air quality improvement.

Seasonal associations between air pollutants and influenza in 10 cities of southern China.

Few studies have explored the associations between air pollutants and influenza across seasons, especially at large scales. This study aimed to evaluate seasons' modifying effects on associations between air pollutants and influenza from 10 cities of southern China. Through scientific evidence, it provides mitigation and adaptation strategies with practical guidelines to local health authorities and environmental protection agencies. Daily influenza incidence, meteorological, and air pollutants data from 2016 to 2019 were collected. Quasi-Poisson regression with a distributed lag nonlinear model was used to evaluate city-specific air pollutants and influenza associations. Meta-analysis was used to pool site-specific estimates. Attributable fractions (AFs) of influenza incidence due to pollutants were calculated. Stratified analyses were conducted by season, sex, and age. Overall, the cumulative relative risk (CRR) of influenza incidence for a 10-unit increase in PM2.5, PM10, SO2, NO2, and CO was 1.45 (95% CI: 1.25, 1.68), 1.53 (95% CI: 1.29, 1.81), 1.87 (95% CI: 1.40, 2.48), 1.74 (95% CI: 1.49, 2.03), and 1.19 (95% CI: 1.04, 1.36), respectively. Children aged 0-17 were more sensitive to air pollutants in spring and winter. PM10 had greater effect on influenza than PM2.5 in autumn, winter, and overall, lesser in spring. The overall AF due to PM2.5, PM10, SO2, NO2, and CO was 4.46% (95% eCI: 2.43%, 6.43%), 5.03% (95% eCI: 2.33%, 7.56%), 5.36% (95% eCI: 3.12%, 7.58%), 24.88% (95% eCI: 18.02%, 31.67%), and 23.22% (95% eCI: 17.56%, 28.61%), respectively. AF due to O3 was 10.00% (95% eCI: 4.76%, 14.95%) and 3.65% (95% eCI: 0.50%, 6.59%) in spring and summer, respectively. The seasonal variations in the associations between air pollutants and influenza in southern China would provide evidence to service providers for tailored intervention, especially for vulnerable populations.

Weather Types and Their Influence on PM10 and O3 Urban Concentrations in the Cergy-Pontoise Conurbation

Abstract Daily atmospheric concentrations of the pollutants PM10 and O3 vary according to weather types. This study aims to identify the weather patterns associated with PM10 and O3 pollution episodes from 2009 to 2020. Episodes characterized by exceedance of World Health Organization standards were identified, and their duration and persistence were studied. The results show that air pollution days are associated with three atmospheric patterns for PM10 and four for O3. The dominant weather pattern corresponds to an anticyclonic situation in central and Eastern Europe with a ridge of high pressure over France at the surface and 500-hPa geopotential height. For PM10, the persistent high-concentration sequences were found to be associated with a thermal inversion constraining the vertical dispersion of pollutants. For O3, the four weather types responsible for ozone pollution all have a higher occurrence in summer. The highest percentage (46% of days) is associated with the presence of a ground-level barometric marsh (an area of the atmosphere between two weather systems where the pressure varies slightly but is slightly low) and a ridge at 500 hPa (weather type T1). Similarly, thermal inversions and thermal winds cause pollution to persist beyond 8 consecutive days. Significance Statement Air quality is not only influenced by ground-level emissions, but also by complex meteorological processes that can contribute to pollutant accumulations. The importance of this research is that the prediction of these processes helps to prevent the development of extreme concentrations near the surface. The results of this study provide a better understanding of how characteristic weather patterns in the Cergy-Pontoise conurbation impact PM10 and O3 pollutant levels. These impacts are expressed by the intensity and frequency of pollution episodes.

The use of asthma and allergy medicines is associated with exposure to smoking

Exposure to ambient air pollution and tobacco smoking have been reported to trigger asthma and allergies, but these risk factors have typically not been assessed together. Here we collected and analysed daily wastewater samples from an urban population of approximately 500,000 people in Southern China between November 2017 and October 2018 for biomarkers of asthma and allergy medicines and exposure to tobacco smoke. Daily air pollution data (PM2.5, PM10, SO2, NO2, CO and O3) were obtained from the nearest air quality monitoring station. Using time-series linear regression, we show a significantly higher use of antihistamine medicines in spring and in autumn, associated with peak pollen levels, but with no significant association between the use of asthma and allergy medications and ambient air pollution levels. There were significant positive associations between biomarkers of exposure to tobacco smoke and the use of selected medicines. Wastewater-based epidemiology can be a cost-effective way to assess the health impacts of multiple environmental factors at the population level. The exposure to both air pollution and cigarette smoke can trigger asthma. Wastewater surveillance was used to demonstrate the possibility of distinguishing between these two factors.

PREDICTION OF AQI USING HYBRID APPROACH IN MACHINE LEARNING

Forecast of urban air pollutant concentrations must deal with a growth in the volume of environmental monitoring data as well as complex changes in the air pollutants. This initiates the need of efficient prediction techniques to increase prediction accuracy and stop pollution causing issues. In this paper, the combination of Random Forest Regression (RFR) and Support Vector Regression (SVR) based machine learning model is proposed to predict the AQI values. Daily meteorological and air pollution data between March 2020 and June 2022 from Jind city in Haryana were used for model training and test the model’s results. First, the important factors affecting air quality are extracted and null values are replaced by their mean values to handle the irregularities in the dataset. Second, the Random Forest regression machine is used for model training and prediction of the value, the SVR model is used in correction of residual items, and finally the predicted values of AQI are obtained. The experimental results showed that the proposed prediction model of RFR-SVR had a better prediction result than the standard Random Forest Regression, support vector regression machine learning. RMSE, R2, MAPE are used as evaluation indicators to evaluate the performance of the proposed model.

Use of symptom diary in primary students: association of nitrogen dioxide with prevalence of symptoms.

Air pollution is a global public health concern, and numerous studies have attempted to identify the health effects of air pollutants, including nitrogen dioxide (NO2). In China, there are few studies investigating the relationship between NO2 exposure and symptoms among children at an individual level. The aim of the study was to evaluate the acute effects of NO2 on prevalence of symptoms of primary students. An environmental and health questionnaire survey was administered to 4240 primary students in seven districts of Shanghai. Daily symptoms, as well as the daily air pollution and meteorological data from each community, were recorded during the corresponding period. A multivariable logistic regression model was utilized to analyze the relationship between the prevalence of symptoms and NO2 exposure in school-age children. A model with interaction items was adopted to estimate the interactive effects of NO2 and confounding factors on symptoms. The average NO2 level in central urban, industrial and rural areas were 62.07 ± 21.66, 54.86 ± 18.32 and 36.62 ± 21.23μgm-3, respectively. Our findings demonstrate that the occurrence of symptoms was significantly affected by NO2 exposure in the short-term. The largest associations were observed for a 10μgm-3 increase in 5-day moving average (lag04) NO2 concentration with prevalence of general symptoms (odds ratio [OR] = 1.15, 95% confidence interval [95% CI]: 1.07-1.22), throat symptoms (OR = 1.23, 95% CI: 1.13-1.35) and nasal symptoms (OR = 1.142, 95% CI: 1.02-1.27). Subgroup analysis showed that non-rural areas, boys, nearby environmental pollution source and history of present illness were all susceptible factors to the effects of NO2 exposure. Furthermore, there were interactive effects between NO2 exposure and area types on reported symptoms. NO2 can increase the risk of symptoms in primary students in the short-term, which could be significantly enhanced in central urban and industrial areas.

Short-term effects of air pollution on hospital admissions for cardiovascular diseases and diabetes mellitus in Sofia, Bulgaria (2009–2018)

Bulgaria has a very high incidence of cardiometabolic diseases and air pollution-related mortality rate. This study investigated the relationship between daily air pollution levels and hospital admissions for ischaemic heart diseases (IHD), cerebral infarction (CI), and type 2 diabetes mellitus (T2DM) in Sofia, Bulgaria. We obtained daily data on hospitals admissions and daily average air pollution levels from 2009 to 2018. Pollutants of interest were particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Negative binomial regressions were fitted to study the effects of air pollution on hospital admission over the course of seven days prior to that event, accounting for autocorrelations and time trend in the data, day of the week, temperature, and relative humidity. Our findings confirm that higher air pollution levels generally increase the risk of hospital admissions for IHD and CI. For T2DM the association is less clear. Admissions often lagged several days behind and were more common in specific demographic subgroups or when pollution crossed a particular threshold. However, we did not expect to find the risk of hospital admissions increased in warmer rather than colder months of the year. Our findings are to be taken with reservation but do provide an idea about how air pollution could trigger acute episodes of related cardiovascular diseases, and our model may serve to investigate similar associations across the country.

Air pollution and its effects on emergency room visits in tertiary respiratory care centers in Delhi, India.

Environmental pollution has harmful effects on human health, particularly the respiratory system. We aimed to study the impact of daily ambient air pollution on daily emergency room visits for acute respiratory symptoms. This study was conducted in two tertiary respiratory care centres in Delhi, India. Daily counts of emergency room visits were collected. All patients attending the emergency room were screened for acute onset (less than 2 weeks) of respiratory symptoms and were recruited if they were staying in Delhi continuously for at least 4 weeks and having onset (≤2 weeks) of respiratory symptoms. Daily average air pollution data for the study period was obtained from four continuous ambient air quality monitoring stations. A total of 61,285 patients were screened and 11,424 were enrolled from June 2017 to February 2019. Cough and difficulty in breathing were most common respiratory symptoms. Poor air quality was observed during the months of October to December. Emergency room visits with acute respiratory symptoms significantly increased per standard deviation increase in PM10 from lag days 2-7. Increase in wheezing was primarily seen with increase in NO2. Pollutant levels have effect on acute respiratory symptoms and thus influence emergency room visits. *************************************************************** *Appendix Authors list Kamal Singhal,1 Kana Ram Jat,2 Karan Madan,3 Mohan P. George,4 Kalaivani Mani,5 Randeep Guleria,3 Ravindra Mohan Pandey,5 Rupinder Singh Dhaliwal,6 Rakesh Lodha,2 Varinder Singh1 1Department of Paediatrics, Lady Hardinge Medical College and associated Kalawati Saran Children's Hospital, New Delhi, India 2Department of Paediatrics, All India Institute of Medical Sciences, New Delhi, India 3Department of Pulmonary Medicine, Critical Care and Sleep Disorders, All India Institute of Medical Sciences, New Delhi, India 4Department of Environment, Delhi Pollution Control Committee, Kashmere Gate, New Delhi, India 5Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India 6Department of Non-communicable Diseases, Indian Council of Medical Research, New Delhi, India.

Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.

Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM2.5 (r = 0.097), PM10 (r = 0.215) and O3 (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO2 (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM10, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM10, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.

Urban landscape and climate affect residents’ sentiments based on big data

Residents’ sentiments are a quantitative indicator of human feelings, which is useful for optimizing urban residential environments. Little is known about the spatiotemporal variations and potential drivers of sentiment based on big data. A total of 221,104 Weibo social media data were used to quantify daily sentiment in the Beijing metropolitan area during the COVID-19 pandemic from January 1, 2021 to March 8, 2022. Deep-learning natural language processing was used to extract this dataset to investigate the spatiotemporal sentiment patterns. The density of roads and buildings, normalized difference vegetation index (NDVI), population, sky visibility factor, daily land surface temperature (LST), daily precipitation, and daily air pollution concentrations (CO, NO2, PM2.5, SO2, and O3) were explored as potential drivers of sentiment. Results show that (1) the holiday sentiment was 1.31% higher than on weekends and 4.61% higher than on weekdays. Extreme precipitation, air pollution, and COVID-19 lockdown measures have reduced sentiment. (2) The sentiment in spring was found to be the highest. The numbers of functional zones with high sentiment values (>0.8) in spring were 13.59%, in summer 34.48%, and in autumn 14.71%. (3) Sentiment was highest under conditions of moderate greenness (0.4<NDVI<0.6) and comfortable daily temperature (25 °C < LST<30 °C). (4) Sentiment was negatively associated with daily air pollutants, such as PM2.5, NO2, and CO. This paper presents the effectiveness of sentiment quantification based on social media data and deep-learning techniques. The results provide practical implications and support decisions for sustainable urban health development.

Environmental pollution in North-Eastern Italy and its influence on chronic obstructive pulmonary disease: time series modelling and analysis using visibility graphs

The impact on human health from environmental pollution is receiving increasing attention. In the case of respiratory diseases such as chronic obstructive pulmonary disease (COPD), the relationship is now well documented. However, few studies have been carried out in areas with low population density and low industrial production, such as the province of Belluno (North-Eastern Italy). The aim of the study was to analyze the effect of exposure to certain pollutants on the temporal dynamics of hospital admissions for COPD in the province of Belluno. Daily air pollution concentration, humidity, precipitations, and temperature were collected from the air monitoring stations in Belluno. Generalized additive mixed models (GAMM) and visibility graphs were used to determine the effects of the short-term exposure to environmental agents on hospital admissions associated to COPD. In the case of the city of Belluno, the GAMM showed that hospital admissions were associated with NO2, PM10, date, and temperature, while for the city of Feltre, GAMM produced no associated variables. Several visibility graph indices (average edge overlap and interlayer mutual information) showed a significant overlap between environmental agents and hospital admission for both cities. Our study has shown that visibility graphs can be useful in establishing associations between environmental agents and COPD hospitalization in sparsely populated areas.

Associations between Different Ozone Indicators and Cardiovascular Hospital Admission: A Time-Stratified Case-Crossover Analysis in Guangzhou, China.

Epidemiological studies reported that ozone (O3) is associated with cardiovascular diseases. However, only few of these studies examined the impact of multiple O3 indicators on cardiovascular hospital admissions. This study aimed to explore and compare the impacts of different O3 indicators on cardiovascular hospital admissions in Guangzhou, China. Based upon the data on daily cardiovascular hospital admissions, air pollution, and meteorological factors in Guangzhou from 2014 to 2018, a time-stratified case-crossover design model was used to analyze the associations between different O3 indicators and cardiovascular hospital admissions. Moreover, the sensitivities of different age and gender groups were analyzed for the whole year and different seasons (i.e., warm and cold). During the warm season, for the single-pollutant model, the odds ratio (OR) value of cardiovascular hospital admissions was 1.0067 (95% confidence interval (CI): 1.0037, 1.0098) for every IQR increase in MDA8 O3 at a lag of five days. The effect of O3 on people over 60 year was stronger than that on the 15-60 years age group. Females were more sensitive than males to O3 exposure. These results provided valuable references for further scientific research and environmental improvement in Guangzhou. Given that short-term O3 exposure poses a threat to human health, the government should therefore pay attention to prevention and control policies to reduce and eliminate O3 pollution and protect human health.

COMPARATIVE ASSESSMENT OF SANITARY AND HYGIENIC NORMS OF HOUSING PIGS ACCORDING TO DIFFERENT PARATYPICAL FACTORS

Introduction. The relevance of the work is determined by the implementation of the method of multi-parametric assessment of the microclimate of premises for pigs by the method of continuous automatic registration. The purpose o f the research was to conduct a comparative assessment of indoor pollution with carbon dioxide, ammonia, hydrogen sulfide, methane, and fine dust based on various paratypical factors. Research methods. Analytical, zootechnical, physical, ethological and biometric research methods are applied. Research results. A comparative assessment of the sanitary and hygienic norms for housing pigs in brick premises on concrete and slotted floors in machines was carried out. It was determined that during the periods of the year, the average daily CO2 and NH3 air pollution in pig houses with concrete and slotted floors in the machines were close to the normative values of MPC. The volume concentration of methane was 1.9-3.4 times higher in the premise with a slotted floor in the machines, which is associated with the features of the technology of the self-alloying system of manure removal. According to the calculations of the correlation between the temperature and the relative humidity of the air outside and inside the premises, a probable mostly positive connection was determined. The locomotor activity indices of young pigs were 9.6-12.4 % higher on the slatted floor, but they were inferior in the food activity index by 6.4-16.2 % to peers kept on the concrete floor. The variance analysis determined the probable influence fi=0.784) of the method of housing pigs on solid concrete and slotted floors in machines on the variability of the volume concentration of methane. It was also found out that the period of the year likely influenced the variability of air temperature, volume concentration of carbon dioxide, ammonia and mass concentration of fine dust in the room fi= 0.799; 0.794; 0.352 and 0.439, respectively). Conclusions. The average daily volume concentration in the air of carbon dioxide and ammonia in pig houses by periods of the year approached the normative values of the MPC. The volume concentration of methane was higher in the premise with a slotted floor, which is related to the features of the technology of the self-alloying manure removal system. The probable influence of the method of housing pigs on the variability of indicators of relative air humidity and volume concentration of methane wasdetermined. The probable influence of the period of the year on the variability of air temperature indicators, volume concentration of carbon dioxide, ammonia and mass concentration of fine dust has also been proven. Key words: measuring and computing complex, microclimate parameters, comparative assessment, premise for pigs, paratypic factor

Early prenatal exposure to air pollutants and congenital heart disease: a nested case-control study

Congenital heart disease (CHD) is one of the most common congenital malformations in humans. Inconsistent results emerged in the existed studies on associations between air pollution and congenital heart disease. The purpose of this study was to evaluate the association of gestational exposure to air pollutants with congenital heart disease, and to explore the critical exposure windows for congenital heart disease. The nested case-control study collected birth records and the following health data in Tianjin Women and Children's Health Center, China. All of the cases of congenital heart disease from 2013 to 2015 were selected matching five healthy controls for each case. Inverse distance weighting was used to estimate individual exposure based on daily air pollution data. Furthermore, the conditional logistic regression with distributed lag non-linear model was performed to identify the association between gestational exposure to air pollution and congenital heart disease. A total of 8,748 mother-infant pairs were entered into the analysis, of which 1,458 infants suffered from congenital heart disease. For each 10 µg/m3 increase of gestational exposure to PM2.5, the ORs (95% confidence interval, 95%CI) ranged from 1.008 (1.001-1.016) to 1.013 (1.001-1.024) during the 1st-2nd gestation weeks. Similar weak but increased risks of congenital heart disease were associated with O3 exposure during the 1st week and SO2 exposure during 6th-7th weeks in the first trimester, while no significant findings for other air pollutants. This study highlighted that gestational exposure to PM2.5, O3, and SO2 had lag effects on congenital heart disease. Our results support potential benefits for pregnancy women to the mitigation of air pollution exposure in the early stage, especially when a critical exposure time window of air pollutants may precede heart development.

Role of air pollution on COVID-19 in Istanbul

There are many types of research on strong relations between air pollution and the respiration system. This paper is related to the effect of air pollution on pandemic case numbers. By analyses of the number of patients and mean air pollution (µg/m³) data by using data mining, it is concluded that there was evidence of this relationship with a significant level alpha = 0,10. This paper estimates "the number of patients with coronavirus" as a function of daily air pollution and corona case numbers data using single ANN, LSTM, and W-ANN, W-LSTM hybrid methods. This paper explains small, meso, and large scale factors and their role on COVID-19 patients. This finding does not demonstrate a direct cause-effect relationship between air pollution and COVID-19 patients. This study shows the importance of pollution on the number of patients with COVID -19 infection. Although our results have significant uncertainties, we can clearly distinguish the contribution of air pollution to COVID-19 patients. When we used wavelet transformation of air pollution data to estimate COVID-19 patient’s numbers, R2 score is increased in both ANN and LSTM between [0.01- 0.10]. Nevertheless, the actual number of patients is influenced by many additional factors such as the country's health system. After error analysis, sMAPE [3.6-5.9] changed, there is sufficient evidence of model results (M3, M4 Hybrid LSTM) and observation. (0.91

Effect modification by temperature on the association between O3 and emergency ambulance dispatches in Japan: A multi-city study

Numerous epidemiological studies have reported that ozone (O3) and temperature are independently associated with health outcomes, but modification of the effects of O3 on health outcomes by temperature, and vice versa, has not been fully described. This study aimed to investigate effect modification by temperature on the association between O3 and emergency ambulance dispatches (EADs) in Japan. Data on daily air pollutants, ambient temperature, and EADs were obtained from eight Japanese cities from 2007 to 2015. A distributed lag non-linear model combined with Poisson regression was performed with temperature as a confounding factor and effect modifier to estimate the effects of O3 on EADs at low (<25th percentile), moderate (25th–75th percentile), and high (>75th percentile) temperature for each city. The estimates obtained from each city were pooled by random-effects meta-analysis. When temperature was entered as a confounder, the estimated effects of O3 on EADs for all acute, cardiovascular, and respiratory illnesses were largest at lag 0 (current-day lag). Therefore, this lag was used to further estimate the effects of O3 on EADs in each temperature category. The estimated effects of O3 on EADs for all acute, cardiovascular, and respiratory illnesses in all eight Japanese cities increased with increasing temperature. Specifically, a 10 ppb increase in O3 was associated with 0.80 % (95 % CI: 0.25 to 1.35), 0.19 % (95 % CI: −0.85 to 1.25), and 1.14 % (95 % CI: −0.01 to 2.31) increases in the risk of EADs for all acute, cardiovascular, and respiratory illnesses, respectively, when city-specific daily temperature exceeded the 75th percentile. Our findings suggest that the association between O3 and EADs for all acute, cardiovascular, and respiratory illnesses is the highest during high temperature. Finding of this study can be used to develop potential mitigation measures against O3 exposure in high temperature environment to reduce its associated adverse health effects.

The associations between short-term exposure to ambient particulate matter and hospitalizations for osteoporotic fracture in Hangzhou: a time-stratified case-crossover study.

Osteoporotic fractures are accompanied by high rates of disability and mortality. PM has been linked with many health outcomes. However, few studies focus on the association of short-term exposure to ambient PM and osteoporotic fractures. Data on daily mean air pollution, meteorological factors, and hospitalizations for osteoporotic fractures were collected from Hangzhou, China, 2020-2021. A time-stratified case-crossover design with extended Cox proportional hazards regression was applied to assess the associations between PM and osteoporotic fractures. Short-term exposure to PM significantly increased the risks of hospitalizations for osteoporotic fractures at cumulative lag days. Per 10μg/m3 increased in PM2.5 (PM with an aerodynamic diameter ≤ 2.5μm), PMC (PM with an aerodynamic diameter between 2.5μm and 10μm), and PM10 (PM with an aerodynamic diameter ≤ 10μm) were associated with 5.65% (95% confidence intervals (CIs): 1.29, 10.19), 3.19% (0.11, 6.36), and 2.45% (0.57, 4.37) increase in hospitalizations for osteoporotic fractures, respectively. Significant PM-osteoporotic fracture associations were only observed in females and people aged over 65years old. For the season, the estimates of PM on hospitalizations for osteoporotic fractures were 6.30% (95% CIs: 1.62, 11.20) in the cold season vs. 2.16% (95% CIs: - 4.62, 9.42) in the warm season for per 10μg/m3 increase of PM2.5, and 0.99 (95% CIs: - 2.69, 4.80) vs. 6.72% (95% CIs: 0.68, 13.13) for PMC. Our study showed PM was positively linked with the risk of osteoporotic fractures. Females and people aged over 65years old were more susceptible to PM. The adverse impacts of PM2.5 in the cold season and PMC in the warm season were worthy of special attention.

Trends of ambient O3 levels associated with O3 precursor gases and meteorology in California: Synergies from ground and satellite observations

This study investigated the trends of ambient ozone (O3) levels and their associations with precursor gases and meteorology using ground and satellite observations in California during the peak O3 seasons of 2005-2017. The decrease in ground-level nitrogen dioxide (NO2) concentrations due to air pollution control strategies improved the O3 air quality by 0.67 ppb/year on average, while local meteorology worsened the O3 air quality by 0.11 ppb/year. The improvement of O3 air quality that was attributed to NO2 was more pronounced on higher O3 air pollution days with the largest improvement for the 90th percentile of O3 (0.85 ppb/year). The O3 trends were modified more by the reductions of NO2 (a proxy of nitrogen oxides (NOx)) than those of non-methane organic compounds (NMOC; a proxy of volatile organic compounds (VOCs)). The highest and lowest contributions of NO2 relative to NMOC to the reduction of O3 were found in the Sacramento Valley and South Coast, respectively. Satellite Ozone Monitoring Instrument (OMI) formaldehyde (HCHO) to NO2 ratios, an indicator of O3 sensitivity to precursor gases, were the lowest in the South Coast (specifically the corridor connecting downtown Los Angeles and Riverside), suggesting the highest potential of O3 to be influenced by VOCs. In addition, the OMI HCHO/NO2 ratios increased over time across California (0.23 per year), which demonstrated that the O3 air quality was increasingly sensitive to NOx. The satellite-based finding was consistent with ground NO2-NMOC-O3 associations concerning the direction and relative extent of NO2 and NMOC contributions to the O3 trends. Therefore, the NOx emission controls are expected to continue mitigating O3 levels and protect public health from adverse health outcomes associated with O3 and also NO2. Nonetheless, the O3 air quality may further benefit from local-scale strategies for VOC controls in certain localized areas (e.g., high traffic volumes).