Hourly associations between ambient air pollution and emergency ambulance calls in one central Chinese city: Implications for hourly air quality standards

Incidence of cough from acute exposure to fine particulate matter (PM2.5) in Madagascar: A pilot study

Prolonged exposure to fine particulate matter (PM2.5) is a known risk to respiratory health, causing chronic lung impairment. Yet, the immediate, acute effects of PM2.5 exposure on respiratory symptoms, such as cough, are less understood. This pilot study aims to investigate this relationship using objective PM2.5 and cough monitors. Fifteen participants from rural Madagascar were followed for three days, equipped with an RTI Enhanced Children's MicroPEM PM2.5 sensor and a smartphone with the ResApp Cough Counting Software application. Univariable Generalized Estimating Equation (GEE) models were applied to measure the association between hourly PM2.5 exposure and cough counts. Peaks in both PM2.5 concentration and cough frequency were observed during the day. A 10-fold increase in hourly PM2.5 concentration corresponded to a 39% increase in same-hour cough frequency (incidence rate ratio (IRR) = 1.40; 95% CI: 1.12, 1.74). The strength of this association decreased with a one-hour lag between PM2.5 exposure and cough frequency (IRR = 1.21; 95% CI: 1.01, 1.44) and was not significant with a two-hour lag (IRR = 0.93; 95% CI: 0.71, 1.23). This study demonstrates the feasibility of objective PM2.5 and cough monitoring in remote settings. An association between hourly PM2.5 exposure and cough frequency was detected, suggesting that PM2.5 exposure may have immediate effects on respiratory health. Further investigation is necessary in larger studies to substantiate these findings and understand the broader implications.

Improved prediction of hourly PM2.5 concentrations with a long short-term memory and spatio-temporal causal convolutional network deep learning model

Accurate prediction of particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) is important for environmental management and human health protection. In recent years, many efforts have been devoted to develop air quality predictions using the machine learning and deep learning techniques. In this study, we propose a deep learning model for short-term PM2.5 predictions. The salient feature of the proposed model is that the convolution in the model architecture is causal, where the output of a time step is only convolved with components of the same or earlier time step from the previous layer. The model also weighs the spatial correlation between multiple monitoring stations. Through temporal and spatial correlation analysis, relevant information is screened from the monitoring stations with a strong relationship with the target station. Information from the target and related sites is then taken as input and fed into the model. A case study is conducted in Nanjing, China from January 1, 2020 to December 31, 2020. Using historical air quality and meteorological data from nine monitoring stations, the model predicts PM2.5 concentrations for the next hour. The experimental results show that the predicted PM2.5 concentrations are consistent with observation, with correlation coefficient (R2) and Root Mean Squared Error (RMSE) of our model are 0.92 and 6.75 μg/m3. Additionally, to better understand the factors affecting PM2.5 levels in different seasons, a machine learning algorithm based on Principal Component Analysis (PCA) is used to analyze the correlations between PM2.5 and its influencing factors. By identifying the main factors affecting PM2.5 and optimizing the input of the predictive model, the application of PCA in the model further improves the prediction accuracy, with decrease of up to 17.2 % in RMSE and 38.6 % in mean absolute error (MAE). The deep learning model established in this study provide a valuable tool for air quality management and public health protection.

The Combined Effects of Hourly Multi-Pollutant on the Risk of Ambulance Emergency Calls: A Seven-Year Time Series Study

BACKGROUND

Ambulance emergency calls (AECs) are seen as a more suitable metric for syndromic surveillance due to their heightened sensitivity in reflecting the health impacts of air pollutants. Limited evidence has emphasized the combined effect of hourly air pollutants on AECs. This study aims to investigate the combined effects of multipollutants (i.e., PM2.5, PM10, Ozone, NO2, and SO2) on all-cause and cause-specific AECs by using the quantile g-computation method.

METHODS

We used ambulance emergency dispatch data, air pollutant data, and meteorological data from between 1 January 2013 and 31 December 2019 in Shenzhen, China, to estimate the associations of hourly multipollutants with AECs. We followed a two-stage analytic protocol, including the distributed lag nonlinear model, to examine the predominant lag for each air pollutant, as well as the quantile g-computation model to determine the associations of air pollutant mixtures with all-cause and cause-specific AECs.

RESULTS

A total of 3,022,164 patients were identified during the study period in Shenzhen. We found that each interquartile range increment in the concentrations of PM2.5, PM10, Ozone, NO2, and SO2 in 0-8 h, 0-8 h, 0-48 h, 0-28 h, and 0-24 h was associated with the highest risk of AECs. Each interquartile range increase in the mixture of air pollutants was significantly associated with a 1.67% (95% CI, 0.12-3.12%) increase in the risk of all-cause AECs, a 1.81% (95% CI, 0.25-3.39%) increase in the risk of vascular AECs, a 1.77% (95% CI, 0.44-3.11%) increase in reproductive AECs, and a 2.12% (95% CI, 0.56-3.71%) increase in AECs due to injuries.

CONCLUSIONS

We found combined effects of pollutant mixtures associated with an increased risk of AECs across various causes. These findings highlight the importance of targeted policies and interventions to reduce air pollution, particularly for PM, Ozone, and NO2 emissions.

Short-term exposure to various ambient air pollutants and emergency department visits for cause-stable ischemic heart disease: a time-series study in Shanghai, China

Studying the impact of local meteorological conditions and air pollution on cardiovascular disease is crucial for reducing the burden of cardiovascular disease. However, there have been few studies on the acute effects of various air pollutants on stable ischemic heart disease (SIHD), and the effects of these factors are not well defined and require further investigation. We performed a time-series study aimed at exploring the association between short-term exposure to various air pollutants and emergency department (ED) visits for SIHD during 2013-2020 in Baoshan District Renhe Hospital of Shanghai, China. The associations between air pollution (NO2, PM2.5, PM10, SO2 O3-8 h and CO) and ED visits were analyzed using quasi-Poisson regression. Subgroup and sensitivity analyses were conducted. From 2013 to 2020, a total of 18,241 ED visits for SIHD were recorded. Elevated PM2.5, PM10, NO2, SO2 and CO were significantly associated with increased ED visits for SIHD at lag (0, 5), lag 0, lag (0-4, 01-03), lag (0-3, 5, 01-03) and lag (3-5). When the concentration of O3-8 h was lower than the threshold recommended by the WHO, exposure to O3-8 h was associated with a slightly decreased risk of SIHD. Moreover, the relationship between different types of air pollution and the frequency of ED visits exhibited variations based on gender, age, and seasonality. This study suggests that short-term exposure to PM2.5, PM10, NO2, SO2 and CO might induce SIHD, especially in old females. Air pollution control measures should be encouraged to prevent the occurrence and development of SIHD.

Impact of short-term exposure to ambient air pollution on osteoarthritis: a multi-city time-series analysis in Central-Eastern China

Osteoarthritis (OA) is a threat to public health issue with high morbidity and disability worldwide. However, unequivocal evidence on the link between air pollution and OA remains little, especially in multi-study sites. This study aimed to explore the relationship between short-term exposure to main air pollutants and the risk of OA outpatient visits in multi-study sites. A multi-city time-series analysis was performed in Anhui Province, Central-Eastern China from January 1, 2015, to December 31, 2020. We used a two-stage analysis to assess the association between air pollution and daily OA outpatient visits. City-specific associations were estimated with a distributed lag nonlinear model and then pooled by random-effects or fixed-effects meta-analysis. Stratified analysis was conducted by gender, age, and season. Additionally, the disease burden of OA attributable to air pollutant exposure was calculated. A total of 35,700 OA outpatients were included during the study period. The pooled exposure-response curves showed that PM2.5 and PM10 concentrations below the reference values could increase the risk of OA outpatient visits. Concretely, per 10 ug/m3 increase in PM2.5 concentration was linked to an elevated risk of OA outpatient visits at lag 2 and lag 3 days, where the effect reached its highest value on lag 2 day (RR: 1.023, 95%CI: 1.005-1.041). We observed that a 10 μg/m3 increase in PM10 was positively correlated with OA outpatient visits (lag2 day, RR: 1.011, 95%CI: 1.001-1.025). Nevertheless, no statistical significance was discovered in gaseous pollutants (including SO2, O3, and CO). Additionally, a significant difference was found between cold and warm seasons, but not between different genders or age groups. This study reveals that particulate matter is an important factor for the onset of OA in Anhui Province, China. However, there is no evidence of a relationship of gaseous pollutants with OA in this area.

Associations between hourly ambient particulate matter air pollution and ambulance emergency calls among 3,022,164 patients: time stratified case-crossover study

BACKGROUND

Associations between short-term exposure to ambient particulate matter (PM) air pollutants and mortality or hospital admissions have been well documented in previous studies. Less is known about the associations of hourly exposure to PM air pollutants with ambulance emergency calls (AECs) for all causes and specific causes by conducting a case-crossover study. In addition, different patterns of AECs may be attributed to different seasons and daytime/nighttime periods.

OBJECTIVE

In this study, we quantified the risk of all-cause and cause-specific AECs associated with hourly PM air pollutants between 1 January 2013 and 31 December 2019 in Shenzhen, China. We also examined whether the observed associations of particulate matter air pollutants with AECs for all causes differed across strata defined by sex, age, season, and time of day.

METHODS

We used ambulance emergency dispatch data and environmental data between 1 January 2013 and 31 December 2019 from the Shenzhen Ambulance Emergency Centre and the National Environmental Monitor Station to conduct a time-stratified case-crossover study design to estimate the associations of air pollutants (i.e., PM2.5, PM10) with all-cause AECs and cause-specific AECs. We generated a well-established distributed lag nonlinear model for nonlinear concentration response and nonlinear lag response functions. We used conditional logistic regression to estimate odds ratios (ORs) with 95% confidence intervals (CIs), adjusted for public holidays, season, time of day, day of the week, hourly temperature, and hourly humidity to examine the association of all-cause and cause-specific AECs with hourly air pollutant concentrations.

RESULTS

A total of 3,022,164 patients were identified during the study period in Shenzhen. Each IQR increase in PM2.5 (24.0 µg/m3) and PM10 (34.0 µg/m3) concentrations in 24 hours was associated with an increased risk of AECs (PM2.5: all-cause, 1.8%, 95% CI, 0.8%-2.4%; PM10: all-cause, 2.0%, 95% CI, 1.1%-2.9%). We observed a stronger association of all-cause AECs with PM2.5 and PM10 in the daytime than in the nighttime and in the elderly group than in the younger group (PM2.5 daytime, 1.7%, 95% CI, 0.5%-3.0%; nighttime, 1.4%, 95% CI, 0.3%-2.6%; PM10 daytime, 2.1%, 95% CI, 0.9%-3.4%; nighttime, 1.7%, 95% CI, 0.6%-2.8%; PM2.5 18-64 years, 1.4%, 95% CI, 0.6%-2.1%; ≥65 years, 1.6%, 95% CI, 0.6%-2.6%; PM10 18-64 years, 1.8%, 95% CI, 0.9%-2.6%; ≥65 years, 2.0%, 95% CI, 1.1%-3.0%).

CONCLUSIONS

The risk of all-cause AECs increased consistently with increasing concentrations of PM air pollutants, showing a nearly linear relationship with no apparent thresholds. Particulate matter air pollution increase was associated with a higher risk of AECs for all causes, cardiovascular, respiratory, and reproductive AECs. The results of this study may be valuable to air pollution attributable to the distribution of emergency resources, and consistent air pollution control.

CLINICALTRIAL

Short-Term Effect of Moderate Level Air Pollution on Outpatient Visits for Multiple Clinic Departments: A Time-Series Analysis in Xi'an China

There is limited evidence concerning the association between air pollution and different outpatient visits in moderately polluted areas. This paper investigates the effects of moderate-level air pollution on outpatient visits associated with six categories of clinic department. We analyzed a total of 1,340,791 outpatient visits for the pediatric, respiratory, ear-nose-throat (ENT), cardiovascular, ophthalmology, and orthopedics departments from January 2016 to December 2018. A distributed lag nonlinear model was used to analyze the associations and was fitted and stratified by age and season (central heating season and nonheating season). We found SO2 had the largest effect on pediatrics visits (RR = 1.105 (95%CI: 1.090, 1.121)). Meanwhile, PM2.5 and SO2 had greater effects on ENT visits for people under 50 years old. The results showed a strong association between O3 and cardiovascular outpatient visits in the nonheating season (RR = 1.273, 95% CI: 1.189,1.358). The results showed every 10 μg/m3 increase in SO2 was associated with a lower number of respiratory outpatient visits. Significant different associations were observed in PM2.5, NO2, CO, and O3 on ophthalmology visits between the heating and nonheating seasons. Although no significant association has been found in existing studies, our findings showed PM2.5 and NO2 were significantly related to orthopedic outpatient visits for people under 60 (RR = 1.063 (95%CI: 1.032, 1.095), RR = 1.055 (95%CI: 1.011, 1.101)). This study also found that the effect-level concentrations of air pollutants for some clinic departments were lower than the national standards, which means that people should also pay more attention when the air quality is normal.

Vehicle emissions of primary air pollutants from 2009 to 2019 and projection for the 14th Five-Year Plan period in Beijing, China

Over the past decade, the emission standards and fuel standards in Beijing have been upgraded twice, and the vehicle structure has been improved by accelerating the elimination of 2.95 million old vehicles. Through the formulation and implementation of these policies, the emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NO_x), and fine particulate matter (PM_2.5) in 2019 were 147.9, 25.3, 43.4, and 0.91 kton in Beijing, respectively. The emission factor method was adopted to better understand the emissions characteristics of primary air pollutants from combustion engine vehicles and to improve pollution control. In combination with the air quality improvement goals and the status of social and economic development during the 14th Five-Year Plan period in Beijing, different vehicle pollution control scenarios were established, and emissions reductions were projected. The results show that the emissions of four air pollutants (CO, VOCs, NO_x, and PM_2.5) from vehicles in Beijing decreased by an average of 68% in 2019, compared to their levels in 2009. The contribution of NO_x emissions from diesel vehicles increased from 35% in 2009 to 56% in 2019, which indicated that clean and energy-saving diesel vehicle fleets should be further improved. Electric vehicle adoption could be an important measure to reduce pollutant emissions. With the further upgrading of vehicle structure and the adoption of electric vehicles, it is expected that the total emissions of the four vehicle pollutants can be reduced by 20%-41% by the end of the 14th Five-Year Plan period.

The Effect of Sustained Poor Air Quality on EMS Call Volume and Characteristics: A Time-Stratified Case-Crossover Study

OBJECTIVES

As wildfires and air pollution become more common across the United States, it is increasingly important to understand the burden they place on public health. Previous studies have noted relationships between air quality and use of Emergency Medical Services (EMS), but until now, these studies have focused on day-to-day air quality. The goal of this study is to investigate the effect of sustained periods of poor air quality on EMS call characteristics and volume.

METHODS

Using a time-stratified case-crossover design, the effect of exposure to periods of poor air quality on number and type of EMS calls in California, USA from 2014-2019 was observed. Poor air quality periods greater than three days were identified at the United States Environmental Protection Agency's (EPA's) Air Quality Index (AQI) levels of Unhealthy for Sensitive Groups (AQI 100) and Unhealthy (AQI 150). Periods less than three days apart were combined. Each poor air quality period was matched with two one-week controls, the first being the closest preceding week that did not intersect a different case. The second control was the closest week at least three days after the case and not intersecting with a different case. Due to seasonal variation in EMS usage, from the initial cases, cases were used only if it was possible to identify controls within 28 days of the case. A conditional Poisson regression calculated risk ratios for EMS call volume.

RESULTS

Comparing the case periods to the controls, significant increases were found at AQI >100 for total number of calls, and the primary impressions categories of emotional state or behavior, level of consciousness, no patient complaint, other, respiratory, and abdominal. At an AQI >150, significance was found for the primary impressions categories of other, pain, respiratory, and digestive.

CONCLUSION

These data demonstrate increased EMS calls during sustained poor air quality, and that several EMS primary impression categories are disproportionately affected. This study is limited by the imprecision of the primary impression's classification provided by the EMS clinician responding to the EMS call. More research is needed to understand the effects of periods of poor air quality on the EMS system for more efficient deployment of resources.

A systematic review and meta-analysis of intraday effects of ambient air pollution and temperature on cardiorespiratory morbidities: First few hours of exposure matters to life

BACKGROUND

A growing number of studies have reported an increased risk of cardiovascular disease (CVD) and respiratory disease (RD) within hours after exposure to ambient air pollution or temperature. We assemble published evidence on the sub-daily associations of CVD and RD with ambient air pollution and temperature.

METHODS

Databases of PubMed and Web of Science were searched for original case-crossover and time-series designs of English articles examining the intra-day effects of ambient air pollution [particulate matter with aerodynamic diameter ≤2.5 μm (PM_2.5), ≤10 μm (PM₁₀), 2.5-10μm (PM_10-2.5), and < 7 μm (SPM), O₃, SO₂, NO₂, CO, and NO] and temperatures (heat and cold) on cardiorespiratory diseases within 24 h after exposure in the general population by comparing with exposure at different exposure levels or periods. Meta-analyses were conducted to pool excess risks (ERs, absolute percentage increase in risk) of CVD and RD morbidities associated with an increase of 10 μg/m³ in particulate matters, 0.1 ppm in CO, and 10 ppb in other gaseous pollutants.

FINDINGS

Final analysis included thirty-three papers from North America, Europe, Oceania, and Asia. Meta-analysis found an increased risk of total CVD morbidity within 3 h after exposure to PM_2.5 [ER%: 2.65% (95% CI: 1.00% to 4.34%)], PM_10-2.5 [0.31% (0.02% to 0.59%)], O₃ [1.42% (0.14% to 2.73%)], and CO [0.41% (0.01% to 0.81%)]. The risk of total RD morbidity elevated at lag 7-12 h after exposure to PM_2.5 [0.69% (0.14% to 1.24%)] and PM₁₀ [0.38% (0.02% to 0.73%)] and at lag 12-24 h after exposure to SO₂ [2.68% (0.94% to 4.44%)]. Cause-specific CVD analysis observed an increased risk of myocardial infarction morbidity within 6 h after exposure to PM_2.5, PM₁₀, and NO₂, and an increased risk of out-of-hospital cardiac arrest morbidity within 12 h after exposure to CO. Risk of total CVD also increased within 24 h after exposure to heat.

INTERPRETATION

This study supports a sudden risk increase of cardiorespiratory diseases within a few hours after exposure to air pollution or heat, and some acute and highly lethal diseases such as myocardial infarction and cardiac arrest could be affected within a shorter time.

FUNDING

The National Natural Science Foundation of China (Grant No. 42105165; 81773518), the High-level Scientific Research Foundation of Anhui Medical University (Grant No. 0305044201), and the Discipline Construction of Anhui Medical University (Grant No. 0301001836).

A Spatial-Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM2.5 Concentration Prediction

Accurate and fine-grained prediction of PM_2.5 concentration is of great significance for air quality control and human physical and mental health. Traditional approaches, such as time series, recurrent neural networks (RNNs) or graph convolutional networks (GCNs), cannot effectively integrate spatial-temporal and meteorological factors and manage dynamic edge relationships among scattered monitoring stations. In this paper, a spatial-temporal causal convolution network framework, ST-CCN-PM_2.5, is proposed. Both the spatial effects of multi-source air pollutants and meteorological factors are considered via spatial attention mechanism. Time-dependent features in causal convolution networks are extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-PM_2.5 are tuned by Bayesian optimization. Haikou air monitoring station data are employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results include the following points: (1) For a single station, the RMSE, MAE and R² values of ST-CCN-PM_2.5 decreased by 27.05%, 10.38% and 3.56% on average, respectively. (2) For all stations, ST-CCN-PM_2.5 achieve the best performance in win-tie-loss experiments. The numbers of winning stations are 68, 63, and 64 out of 95 stations in RMSE (MSE), MAE, and R², respectively. In addition, the mean MSE, RMSE and MAE of ST-CCN-PM_2.5 are 4.94, 2.17 and 1.31, respectively, and the R² value is 0.92. (3) Shapley analysis shows wind speed is the most influencing factor in fine-grained PM_2.5 concentration prediction. The effects of CO and temperature on PM_2.5 prediction are moderately significant. Friedman test under different resampling further confirms the advantage of ST-CCN-PM_2.5. The ST-CCN-PM_2.5 provides a promising direction for fine-grained PM_2.5 prediction.

Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.

Three exposure metrics of size-specific particulate matter associated with acute lower respiratory infection hospitalization in children: A multi-city time-series analysis in China

BACKGROUND

The global burden of acute lower respiratory infection (ALRI) attributable to air pollution has increased in recent years, but the association between ALRI and exposure to size-specific particulate matter has not been investigated using different exposure metrics.

METHODS

We obtained ALRI admission from seven cities from 2014 to 2016 in China. Different sized particles were measured using three metrics (a) daily mean, (b) hourly peak, and (c) daily excessive concentration hours (DECH). Generalized additive models were fitted for each of the seven cities, and the city-specific estimates were then pooled using random-effects meta-analysis models. Stratified analyses were conducted to examine the effect modifications of gender, age, and season. We also estimated the disease burden due to particulate matter exposures.

RESULTS

There were 111,426 ALRI (79,803 pneumonia and 31,622 bronchiolitis) hospital admissions under the age of 15 between 2014 and 2016 in our study. Daily means were associated with the largest ALRI estimates (95% confidence interval [CI]): 2.43% (0.79%, 4.11%) for PM_2.5, 2.25% (0.11%, 4.44%) for PM_c, and 2.64% (0.73%, 4.58%) for PM₁₀. The magnitude of effect sizes were followed by DECH: 1.94% (0.51%, 3.39%) for PM_2.5, 0.88% (-0.14%, 1.92%) for PM_c, 1.86% (0.50%, 2.01%) for PM₁₀; and hourly peak: 0.70% (-0.60%, 2.01%) for PM_2.5, 1.05% (-0.13%, 2.66%) for PM_c, and 1.20% (-0.20%, 2.62%) for PM₁₀ at lag03. We found significantly higher effects in cold seasons than that in warm seasons, while we did not find a significant different between gender and age groups.

CONCLUSIONS

The adverse effects of exposure to particulate matter on ALRI hospitalizations are reconfirmed. DECH was a possible alternative exposure indicator for PM_2.5 assessment, which may affect air quality standards in the future.

Ambient sulfur dioxide and hospital expenditures and length of hospital stay for respiratory diseases: A multicity study in China

BACKGROUND

Ambient sulfur dioxide (SO2) has been associated with morbidity and mortality of respiratory diseases, however, its effect on length of hospital stays (LOS) and cost for these diagnoses remain unclear.

METHODS

We collected hospital admission information for respiratory diseases from all 11 cities in the Shanxi Province of China during 2017-2019. We assessed individual-level exposure by using an inverse distance weighting approach based on geocoded residential addresses. A generalized additive model was built to delineate city-specific effects of SO2 on hospitalization, hospital expenditure, and length of hospital stay for respiratory diseases. The overall effects were obtained by random-effects meta-analysis. We further estimated the respiratory burden attributable to SO2 by comparing different reference concentrations.

RESULTS

We observed significant effects of SO2 exposure on respiratory diseases. At the provincial level, each 10 μg/m3 increase in SO2 on lag03 was associated with a 0.63% (95% CI: 0.14-0.11) increase in hospital admission, an increase of 4.56 days (95% CI: 1.16-7.95) of hospital stay, and 3647.97 renminbi (RMB, Chinese money) (95% CI: 1091.05-6204.90) in hospital cost. We estimated about 6.13 (95% CI: 1.33-11.10) thousand hospital admissions, 65.77 million RMB (95% CI: 19.67-111.87) in hospital expenditure, and 82.13 (95% CI: 20.87-143.40) thousand days of hospital stay could have potentially been avoided had the daily SO2 concentrations been reduced to WHO's reference concentration (40 µg/m3). Variable values in correspondence with this reference concentration could reduce the hospital cost and LOS of each case by 52.67 RMB (95% CI: 15.75-89.59) and 0.07 days (95% CI: 0.02-0.117).

CONCLUSION

This study provides evidence that short-term ambient SO2 exposure is an important risk factor of respiratory diseases, indicating that continually tightening policies to reduce SO2 levels could effectively reduce respiratory disease burden in Shanxi Province.

Size-Specific Particulate Matter Associated With Acute Lower Respiratory Infection Outpatient Visits in Children: A Counterfactual Analysis in Guangzhou, China

The burden of lower respiratory infections is primarily evident in the developing countries. However, the association between size-specific particulate matter and acute lower respiratory infection (ALRI) outpatient visits in the developing countries has been less studied. We obtained data on ALRI outpatient visits (N = 105,639) from a tertiary hospital in Guangzhou, China between 2013 and 2019. Over-dispersed generalized additive Poisson models were employed to evaluate the excess risk (ER) associated with the size-specific particulate matter, such as inhalable particulate matter (PM₁₀), coarse particulate matter (PM_c), and fine particulate matter (PM_2.5). Counterfactual analyses were used to examine the potential percent reduction of ALRI outpatient visits if the levels of air pollution recommended by the WHO were followed. There were 35,310 pneumonia, 68,218 bronchiolitis, and 2,111 asthma outpatient visits included. Each 10 μg/m³ increase of 3-day moving averages of particulate matter was associated with a significant ER (95% CI) of outpatient visits of pneumonia (PM_2.5: 3.71% [2.91, 4.52%]; PM_c: 9.19% [6.94, 11.49%]; PM₁₀: 4.36% [3.21, 5.52%]), bronchiolitis (PM_2.5: 3.21% [2.49, 3.93%]; PM_c: 9.13% [7.09, 11.21%]; PM₁₀: 3.12% [2.10, 4.15%]), and asthma (PM_2.5: 3.45% [1.18, 5.78%]; PM_c: 11.69% [4.45, 19.43%]; PM₁₀: 3.33% [0.26, 6.49%]). The association between particulate matter and pneumonia outpatient visits was more evident in men patients and in the cold seasons. Counterfactual analyses showed that PM_2.5 was associated with a larger potential decline of ALRI outpatient visits compared with PM_c and PM₁₀ (pneumonia: 11.07%, 95% CI: [7.99, 14.30%]; bronchiolitis: 6.30% [4.17, 8.53%]; asthma: 8.14% [2.65, 14.33%]) if the air pollutants were diminished to the level of the reference guidelines. In conclusion, short-term exposures to PM_2.5, PM_c, and PM₁₀ are associated with ALRI outpatient visits, and PM_2.5 is associated with the highest potential decline in outpatient visits if it could be reduced to the levels recommended by the WHO.

Out-of-hospital cardiac arrest and ambient air pollution: A dose-effect relationship and an association with OHCA incidence

Background

Pollution has been suggested as a precipitating factor for cardiovascular diseases. However, data about the link between air pollution and the risk of out-of-hospital cardiac arrest (OHCA) are limited and controversial.

Methods

By collecting data both in the OHCA registry and in the database of the regional agency for environmental protection (ARPA) of the Lombardy region, all medical OHCAs and the mean daily concentration of pollutants including fine particulate matter (PM₁₀, PM_2.5), benzene (C₆H₆), carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and ozone (O₃) were considered from January 1^st to December 31^st, 2019 in the southern part of the Lombardy region (provinces of Pavia, Lodi, Cremona and Mantua; 7863 km2; about 1550000 inhabitants). Days were divided into high or low incidence of OHCA according to the median value. A Probit dose-response analysis and both uni- and multivariable logistic regression models were provided for each pollutant.

Results

The concentrations of all the pollutants were significantly higher in days with high incidence of OHCA except for O₃, which showed a significant countertrend. After correcting for temperature, a significant dose-response relationship was demonstrated for all the pollutants examined. All the pollutants were also strongly associated with high incidence of OHCA in multivariable analysis with correction for temperature, humidity, and day-to-day concentration changes.

Conclusions

Our results clarify the link between pollutants and the acute risk of cardiac arrest suggesting the need of both improving the air quality and integrating pollution data in future models for the organization of emergency medical services.

Different sized particles associated with all-cause and cause-specific emergency ambulance calls: A multicity time-series analysis in China

BACKGROUND

Compared with mortality and hospital admission, emergency ambulance calls (EACs) could be a more accurate outcome indicator to reflect the health effects of short-term air pollution exposure. However, such studies have been scarce, especially on a multicity scale in China.

METHODS

We estimated the associations of different diameter particles [i.e., inhalable particulate matter (PM₁₀), coarse particulate matter (PM_c), and fine particulate matter (PM_2.5)] with EACs for all-cause, cardiovascular, and respiratory diseases in seven Chinese cities. We collected data on EACs and air pollution from 2014 to 2019. We used generalized additive models and random-effects meta-analysis to examine the city-specific and overall associations. Stratified analyses were conducted to examine the effect modifications of gender, age, and season.

RESULTS

Significant associations of PM₁₀ and PM_2.5 with EACs were observed, while the PM_c associations were positive but not statistically significant in most analyses. Specifically, each 10 μg/m³ increase in 2-day moving average concentration of PM₁₀ was associated with a 0.25% [95% confidence interval (CI): 0.04%, 0.47%] increase in all-cause EACs, 0.13% (95% CI: -0.01%, 0.26%) in cardiovascular EACs, and 0.35% (95% CI: 0.04%, 0.66%) in respiratory EACs. The corresponding increases in daily EACs for PM_2.5 were 0.30% (95% CI, 0.03%, 0.57%), 0.13% (95% CI, -0.07%, 0.33%), and 0.46% (95% CI, 0.01%, 0.92%). Season of the year also modifies the association between particulate matter pollution and EACs.

CONCLUSIONS

Short-term exposure to PM₁₀ and PM_2.5 were positively associated with daily all-cause and respiratory-related EACs. The associations were stronger during warm season than cold season. Our findings suggest that the most harmful fraction of particulate matter pollution is PM_2.5, which has important implications for current air quality guidelines and regulations in China.

Relationship between different air pollutants and total and cause-specific emergency ambulance dispatches in Shanghai, China

OBJECTIVE

Air pollutants play a crucial role in human health and disease. Emergency ambulance dispatch data have excellent potential for public and environmental health research. This study aimed at investigating the impact of short-term exposure to air pollutants on the emergency ambulance dispatches.

METHODS

We used data on emergency ambulance dispatches in Shanghai Municipality, China, from April 1, 2016 to December 31, 2017. The association of the daily emergency ambulance dispatches with air pollutants including PM_2.5 (particles ≤ 2.5 μm in aerodynamic diameter), PM₁₀, O₃, NO₂ and SO₂ was analyzed with the use of time-series analyses.

RESULTS

A total of 310,825 emergency ambulance dispatches for acute illness occurred in Shanghai during the study period. An increase in PM_2.5 by 10 μg/m³ at lag1 and lag2 was shown to increase the risk of emergency ambulance dispatches (RR for lag1 = 1.05, 95% CI 1.00-1.11, RR for lag2 = 1.07, 95% CI 1.01-1.12). PM₁₀, NO₂, and SO₂ also showed significant associations with emergency ambulance dispatches in single-pollutant models. Cause-specific analyses showed an elevation in PM_2.5 by 10 μg/m³ was associated with an increased risk of emergency ambulance dispatches related to respiratory diseases on the current day (lag0, RR 1.17, 95% CI 1.01-1.33), while the impact on emergency ambulance dispatches related to other diseases presented 1-3 days later. The other pollutants have the similar trend.

CONCLUSIONS

Our findings show a strong relationship between ambient air pollutants and emergency ambulance dispatches. Our study contributes to the growing body of evidence describing the adverse health effects of ambient air pollution and will benefit ambulance services for early warning and effective ambulatory planning.

Ambient PM2.5 exposure and hospital cost and length of hospital stay for respiratory diseases in 11 cities in Shanxi Province, China

BACKGROUND

Few studies have examined the effects of ambient particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) on hospital cost and length of hospital stay for respiratory diseases in China.

METHODS

We estimated ambient air pollution exposure for respiratory cases through inverse distance-weighted averages of air monitoring stations based on their residential address and averaged at the city level. We used generalised additive models to quantify city-specific associations in 11 cities in Shanxi and a meta-analysis to estimate the overall effects. We further estimated respiratory burden attributable to PM2.5 using the standards of WHO (25 µg/m3) and China (75 µg/m3) as reference.

RESULTS

Each 10 µg/m3 increase in lag03 PM2.5 corresponded to 0.53% (95% CI: 0.33% to 0.73%) increase in respiratory hospitalisation, an increment of 3.75 thousand RMB (95% CI: 1.84 to 5.670) in hospital cost and 4.13 days (95% CI: 2.51 to 5.75) in length of hospital stay. About 9.7 thousand respiratory hospitalisations, 132 million RMB in hospital cost and 145 thousand days of hospital stay could be attributable to PM2.5 exposures using WHO's guideline as reference. We estimated that 193 RMB (95% CI: 95 to 292) in hospital cost and 0.21 days (95% CI: 0.13 to 0.30) in hospital stay could be potentially avoidable for an average respiratory case.

CONCLUSION

Significant respiratory burden could be attributable to PM2.5 exposures in Shanxi Province, China. The results need to be factored into impact assessment of air pollution policies to provide a more complete indication of the burden addressed by the policies.

Impacts of Short-Term Fine Particulate Matter Exposure on Blood Pressure Were Modified by Control Status and Treatment in Hypertensive Patients

[Figure: see text].

Acute effects of personal exposure to fine particulate matter on salivary and urinary biomarkers of inflammation and oxidative stress in healthy adults

Non-invasive bio-samples, such as saliva and urine, are promising tools for assessment of inflammation and oxidative stress biomarkers. Few studies have investigated potential responses of those biomarkers towards short-term PM_2.5 exposure. We conducted a longitudinal study with 4 repeated examinations among 40 healthy, nonsmoking adults in Shanghai, China. Personal samplings were performed for PM_2.5 exposure assessment. Then, five biomarkers, including C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), alpha-1 antitrypsin (A1AT) in saliva and 8-Iso-Prostaglanding F_2α (8-iso-PGF_2α), total antioxidant capacity (TAC) in urine, were measured. We fitted linear mixed-effect models to evaluate short-term effect of personal PM_2.5 exposure on salivary and urinary biomarkers, adjusting for potential confounders of meteorology, sociodemographic characteristics and biomarker detection. We also explored sensitive time windows of exposure for different biomarkers. We found robust associations of salivary CRP, TNF-α, and urinary 8-iso-PGF_2α with PM_2.5 exposure, and responses of salivary inflammatory markers occurred more acutely than urinary oxidative stress markers. For instance, a 10 μg/m³ increase in PM_2.5 was associated with an elevation of 5.49% (95% CI: 1.17%, 9.99%) in CRP and 7.05% (95% CI: 1.29%, 13.13%) in TNF-α both at lag 12 h, and 6.97% (95% CI: 1.33%, 12.92%) in 8-iso-PGF_2α at lag 01 d. Based on non-invasive samples, this study provided evidence on effect of PM_2.5 exposure on responses of systematic inflammation and oxidative stress. Sub-daily (6-12 h) and daily (≥24 h) period after PM_2.5 exposure might be sensitive time window to detect the responses of salivary (i.e. CRP, TNF) and urinary biomarkers (i.e. 8-iso-PGF_2α), respectively.

Effects of extreme temperatures, fine particles and ozone on hourly ambulance dispatches

There is a dearth of research on the hourly risk of ambulance dispatches with respect to ambient conditions. We evaluated hourly relative risks (RR) and 95% confidence interval (CI) of ambulance dispatches in Taiwan to treat respiratory distress, coma and unconsciousness, and out-of-hospital cardiac arrest (OHCA), from 2006 to 2015. We considered island-wide ambient temperatures, fine particulate matter (PM_2.5), and ozone (O₃) at lag 0-180 h while using a distributed lag nonlinear model and meta-analysis. Results showed the pooled risks peaked at lag 16-18 h for all ambulance dispatches at 99th percentile of hourly temperature (32 °C, versus reference temperature of 25 °C), with significant excess risk of 0.11% (95% CI; 0.06, 0.17) for coma and unconsciousness, and 0.06% (95% CI; 0.01, 0.11) for OHCA. The risks of exposure to 90th percentile of hourly O₃ of 52.3 ppb relative to the Q1 level of 17.3 ppb peaked at lag 14 h, with excess risk of 0.17% (95% CI; 0.11, 0.23) for respiratory distress, 0.11% (95% CI; 0.06, 0.16) for coma and unconsciousness, and 0.07% (95% CI; 0.01, 0.14) for OHCA. The population exposed to reference temperatures of 28 °C, 20 °C, and 26 °C were exposed to the lowest levels of ambulance dispatches risk for respiratory distress, coma and unconsciousness, and OHCA, respectively; the highest cumulative 0-96 h RRs of ambulance dispatches were 1.27 (95% CI; 1.19, 1.35) for OHCA at 5th percentile temperatures and 1.25 (95% CI; 1.11, 1.41) for OHCA at 99th percentile temperatures. Following an accumulating lag of 0-96 h, no significant risk was identified for hourly levels of PM_2.5 and O₃. In conclusion, the analytical results of hourly data speak to immediate and real-time responses to environmental changes, rather than to short-term relationships. In our analyses, we emphasized health events in extreme heat; thus, we recommend a comparative study of daily versus hourly associations.

Risk of ambulance services associated with ambient temperature, fine particulate and its constituents

Short-term adverse health effects of constituents of fine particles with aerodynamic diameters less than or equal to 2.5 μm (PM_2.5) have been revealed. This study aimed to evaluate the real-time health outcome of ambulance services in association with ambient temperature and mass concentrations of total PM_2.5 level and constituents in Kaohsiung City, an industrialized city with the worst air quality in Taiwan. Cumulative 6-day (lag0-5) relative risk (RR) and 95% confidence interval (CI) of daily ambulance services records of respiratory distress, coma and unconsciousness, chest pain, headaches/dizziness/vertigo/fainting/syncope, lying at public, and out-of-hospital cardiac arrest (OHCA) in association with ambient temperature and mass concentrations of total PM_2.5 level and constituents (nitrate, sulfate, organic carbon (OC), and elemental carbon (EC)) from 2006 to 2010 were evaluated using a distributed lag non-linear model with quasi-Poisson function. Ambulance services of chest pain and OHCA were significantly associated with extreme high (30.8 °C) and low (18.2 °C) temperatures, with cumulative 6-day RRs ranging from 1.37 to 1.67 at the reference temperature of 24–25 °C. Daily total PM_2.5 level had significant effects on ambulance services of lying at public and respiratory distress. After adjusting the cumulative 6-day effects of temperature and total PM_2.5 level, RRs of ambulance services of lying at public associated with constituents at 90th percentile versus 25th percentile were 1.35 (95% CI: 1.08, 1.68) for sulfate and 1.20 (95% CI: 1.02, 1.41) for EC, while RR was 1.31 (95% CI: 1.09–1.58) for ambulance services of headache/dizziness/vertigo/fainting/syncope in association with OC at 90th percentile versus 25th percentile. Cause-specific ambulance services had various significant association with daily temperature, total PM_2.5 level, and concentrations of constituents. Elemental carbon may have stronger associations with increased ambulance services than other constituents.

Intraday effects of outdoor air pollution on acute upper and lower respiratory infections in Australian children

Children's respiratory health are particularly vulnerable to outdoor air pollution, but evidence is lacking on the very acute effects of air pollution on the risk of acute upper respiratory infections (AURI) and acute lower respiratory infections (ALRI) in children. This study aimed to evaluate the risk of cause-specific AURI and ALRI, in children within 24 h of exposure to air pollution. We obtained data on emergency cases, including 11,091 AURI cases (acute pharyngitis, acute tonsillitis, acute obstructive laryngitis and epiglottitis, and unspecified acute upper respiratory infections) and 11,401 ALRI cases (pneumonia, acute bronchitis, acute bronchiolitis, unspecified acute lower respiratory infection) in Brisbane, Australia, 2013-2015. A time-stratified case-crossover analysis was used to examine the hourly association of AURI and ALRI with high concentration (95th percentile) of four air pollutants (particulate matters with aerodynamic diameter <10 μm (PM₁₀) and <2.5 μm (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂)). We observed increased risk of acute tonsillitis associated with PM_2.5 within 13-24 h (odds ratio (OR), 1.45; 95% confidence interval [CI], 1.02-2.06) and increased risk of unspecified acute upper respiratory infections related to O₃ within 2-6 h (OR, 1.38, 95%CI, 1.12-1.70), NO₂ within 1 h (OR, 1.19; 95%CI, 1.01-1.40), and PM_2.5 within 7-12 h (OR, 1.21; 95%CI, 1.02-1.43). Cold season and nigh-time air pollution has greater effects on AURI, whereas greater risk of ALRI was seen in warm season and daytime. Our findings suggest exposures to particulate and gaseous air pollution may transiently increase risk of AURI and ALRI in children within 24 h. Prevention measures aimed at protecting children's respiratory health should consider the very acute effects of air pollution.

Intraday effects of ambient PM1 on emergency department visits in Guangzhou, China: A case-crossover study

BACKGROUND

Short-term exposure to PM_2.5 has been widely associated with human morbidity and mortality. However, most up-to-date research was conducted at a daily timescale, neglecting the intra-day variations in both exposure and outcome. As an important fraction in PM_2.5, PM₁ has not been investigated about the very acute effects within a few hours.

METHODS

Hourly data for size-specific PMs (i.e., PM₁, PM_2.5, and PM₁₀), all-cause emergency department (ED) visits and meteorological factors were collected from Guangzhou, China, 2015-2016. A time-stratified case-crossover design with conditional logistic regression analysis was performed to evaluate the hourly association between size-specific PMs and ED visits, adjusting for hourly mean temperature and relative humidity. Subgroup analyses stratified by age, sex and season were conducted to identify potential effect modifiers.

RESULTS

A total of 292,743 cases of ED visits were included. The effects of size-specific PMs exhibited highly similar lag patterns, wherein estimated odds ratio (OR) experienced a slight rise from lag 0-3 to 4-6 h and subsequently attenuated to null along with the extension of lag periods. In comparison with PM_2.5 and PM₁₀, PM₁ induced slightly larger effects on ED visits. At lag 0-3 h, for instance, ED visits increased by 1.49% (95% confidence interval: 1.18-1.79%), 1.39% (1.12-1.66%) and 1.18% (0.97-1.40%) associated with a 10-μg/m³ rise, respectively, in PM₁, PM_2.5 and PM₁₀. We have detected a significant effect modification by season, with larger PM₁-associated OR during the cold months (1.017, 1.013 to 1.021) compared with the warm months (1.010, 1.005 to 1.015).

CONCLUSIONS

Our study provided brand-new evidence regarding the adverse impact of PM₁ exposure on human health within several hours. PM-associated effects were significantly more potent during the cold months. These findings may aid health policy-makers in establishing hourly air quality standards and optimizing the allocation of emergency medical resources.

Effects of hourly levels of ambient air pollution on ambulance emergency call-outs in Shenzhen, China

Some researches have shown the associations between air pollution and hospital-based emergency department visits, while the evidence about the acute effects of air pollution on emergency ambulance dispatches for the whole population is rarely available, especially on an hourly time scale. This paper aimed to investigate the effects of hourly concentrations of ambient air pollution on hourly number of ambulance emergency call-outs (AECOs) in Shenzhen, China. AECO data were collected from Shenzhen Emergency Center from January 2013 to December 2016. A time-stratified case-crossover design with conditional Poisson regression was performed to fit the relationship between hourly air pollution and AECOs. The distributed lag model was applied to determine lag structure of the effects of air pollutants. There were a total of 502,862 AECOs during the study period. The significant detrimental effects of SO₂, PM_2.5, and PM₁₀ appeared immediately with a following harvesting effect after 5 h and the effects lasted for about 96 h. The cumulative effect estimates of four pollutants over 0-96 h were 13.99% (95% CI 7.52-20.85%), 2.07% (95% CI 0.72-3.43%), 1.20% (95% CI 0.54-1.87%), and 2.46% (95% CI 1.63-3.29%), respectively. We did not observe significant effects of O₃. This population-based study quantifies the adverse effects of air pollution on ambulance dispatches and provides evidence of the lag structure of the effects on an hourly time scale.

Relationship between different particle size fractions and all-cause and cause-specific emergency ambulance dispatches

BACKGROUND

Evidence on the relationship between different particle size fractions and emergency ambulance dispatches (EAD) remains limited and sparse.

METHODS

We collected daily data of EAD, ambient air pollution and meteorological data from 2014 to 2018 in Guangzhou, China. We used a generalized additive model with covariate adjustments to estimate the associations between different particle size fractions and EAD related to all-cause, cardiovascular diseases, and respiratory diseases. Several subgroup and sensitivity analyses were also performed.

RESULTS

Significant associations were observed between PM_2.5, PM_2.5-10, PM₁₀ and EADs. A 10 μg/m³ increase of PM_2.5, PM_2.5-10, and PM₁₀ was associated with an increase of 0.98% (95% CI: 0.67, 1.28%), 2.06% (95% CI: 1.44, 2.68%), and 0.75% (95%CI: 0.53, 0.96%) in all-cause EAD, with an increase of 0.69% (95% CI: 0.00, 1.39%), 2.04% (95% CI: 0.64, 3.45%), and 0.60% (95%CI: 0.11,1.10%) in cardiovascular-related EAD, and an increase of 1.14% (95% CI: 0.25, 2.04%), 2.52% (95% CI: 0.72, 4.35%), and 0.89% (95%CI: 0.25,1.52%) in respiratory-related EAD at lag03, respectively. The results were robust in subgroup and sensitivity analyses.

CONCLUSIONS

This study revealed that PM_2.5, PM_2.5-10 and PM₁₀ were significantly related with risks of all-cause and cause-specific EAD. More evidence of high quality may be needed to further support our results in this ecological study.

Air quality, health impacts and burden of disease due to air pollution (PM10, PM2.5, NO2 and O3): Application of AirQ+ model to the Camp de Tarragona County (Catalonia, Spain)

The purpose of this study was to assess the impact to human health of air pollutants, through the integration of different technics: data statistics (spatial and temporal trends), population attributable fraction using AIRQ+ model developed by the WHO, and burden of disease using Disability-Adjusted Life Years (DALYs). The levels of SO₂, NO, NO₂, O₃, H₂S, benzene, PM₁₀, PM_2.5, CO, benzo(a)pyrene and metals, obtained between 2005 and 2017 from the air quality monitoring network across Camp de Tarragona County, were temporally and spatially determined. Health impacts were evaluated using the AIRQ+ model. Finally, the burden of disease was assessed through the calculation of Years of Lost life (YLL) and Years Lost due to Disability (YLD). In general terms, air quality was good according to European quality standards, but it did not fulfil the WHO guidelines, especially for O₃, PM₁₀ and PM_2.5. Several decreasing (NO, NO₂, SO₂, PM₁₀ and benzene) and an increasing (O₃) temporal trend were found. Correlation between unemployment rate and air pollutant levels was found, pointing that the economic crisis (2008-2014) was a factor influencing the air pollutant levels. Reduction of air pollutant levels (PM_2.5) to WHO guidelines in the Camp de Tarragona County would decrease the adult mortality between 23 and 297 cases per year, which means between 0.5 and 7% of all mortality in the area. In this County, for lung cancer, ischemic heart disease, stroke, and chronic obstructive pulmonary disease due to levels of PM_2.5 above the WHO threshold limits, DAYLs were 240 years. This means around 80 DALYs for 100,000 persons every year -between 2005 and 2017. Population attributable fraction (PAF) and burden of disease (DALYs) methodologies are suitable tools for regional and national policymakers, who must take decisions to prevent and to control air pollution and to analyse the cost-effectiveness of interventions.