The construction of the air quality health index (AQHI) and a validity comparison based on three different methods

The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China

The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under three scenarios from the sixth phase of the Coupled Model Intercomparison Project. Our findings indicate there are inversed U-shaped associations for temperature, wind speed, and mixture exposure with step counts, peaking at 11.6 °C in temperature, 2.7 m/s in wind speed, and 30th percentile in mixture exposure. However, there are negative associations between relative humidity and rainfall with step counts. Additionally, relative humidity possesses the highest weights in the joint effect (49% contribution). Compared to 2022s, future step counts are projected to decrease due to temperature changes, while increase due to relative humidity changes. However, when considering both future temperature and humidity changes driven by climate change, the projections indicate a decrease in step counts. Our findings may suggest Chinese physical activity will be negatively influenced by global warming.

Fire-Pollutant-Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons

This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011-2020 wildfire season (June-July-August-September-October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (p-value < 0.05) correlation (r) between RSDxPBI (r RSD = 0.58) and PNEUxPBI (r PNEU = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (r RSD = 0.48), PNEUxPBI (r PNEU = 0.47), COPDxPBI (r COPD = 0.45), CSDxAPI (r CSD = 0.70), RSDxAPI (r CSD = 0.71), PNEUxAPI (r PNEU = 0.49), and COPDxAPI (r PNEU = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.

Air quality health index (AQHI) based on multiple air pollutants and mortality risks in Taiwan: Construction and validation

The currently used air quality index (AQI) is not able to capture the additive effects of air pollution on health risks and reflect non-threshold concentration-response relationships, which has been criticized. We proposed the air quality health index (AQHI) based on daily air pollution-mortality associations, and compared its validity in predicting daily mortality and morbidity risks with the existing AQI. We examined the excess risk (ER) of daily elderly (≥65-year-old) mortality associated with 6 air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3) in 72 townships across Taiwan from 2006 to 2014 by performing a time-series analysis using a Poisson regression model. Random effect meta-analysis was used to pool the township-specified ER for each air pollutant in the overall and seasonal scenarios. The integrated ERs for mortality were calculated and used to construct the AQHI. The association of the AQHI with daily mortality and morbidity were compared by calculating the percentage change per interquartile range (IQR) increase in the indices. The magnitude of the ER on the concentration-response curve was used to evaluate the performance of the AQHI and AQI, regarding specific health outcomes. Sensitivity analysis was conducted using coefficients from the single- and two-pollutant models. The coefficients of PM2.5, NO2, SO2, and O3 associated with mortality were included to form the overall and season-specific AQHI. An IQR increase in the overall AQHI at lag 0 was associated with 1.90%, 2.96%, and 2.68% increases in mortality, asthma, and respiratory outpatient visits, respectively. The AQHI had higher ERs for mortality and morbidity on the validity examinations than the current AQI. The AQHI, which captures the combined effects of air pollution, can serve as a health risk communication tool to the public.

A supplementary assessment system of AQI-V for comprehensive management and control of air quality in chemical industrial parks

Volatile organic compounds (VOCs) are the dominant pollutants in industrial parks. However, they are not generally considered as part of the air quality index (AQI) system, which leads to a biased assessment of pollution in industrial parks. In this study, a supplementary assessment system of AQI-V was established by analyzing VOCs characteristics with vehicle-mounted PTR-TOFMS instrument, correlation analysis and the standards analysis. Three hourly and daily scenarios were considered, and the hierarchical parameter setting was further optimized by field application. The hourly and daily assessments revealed the evaluation factors for the discriminability of different air quality levels, practiced value for regional air quality improvement, and the reservation of general dominant pollutants. Finally, the universality testing in ZPIP successfully recognized most of the peaks, with 54.76%, 38.39% and 6.85% for O₃, VOCs and NO₂ as the dominant pollutant, and reflected the daily ambient air quality condition, together with the dominant pollutant. The AQI-V system with VOCs sub-index is essential for air quality evaluation in industrial parks, which can further provide scientific support to control the pollution of VOCs and the secondary pollutant, therefore significantly improve the air quality in local industrial parks.

Evaluation of the health risk using multi-pollutant air quality health index: case study in Tianjin, China

Introduction

Air pollution imposes a significant burden on public health. Compared with the popular air quality index (AQI), the air quality health index (AQHI) provides a more comprehensive approach to measuring mixtures of air pollutants and is suitable for overall assessments of the short-term health effects of such mixtures.

Methods

We established an AQHI and cumulative risk index (CRI)-AQHI for Tianjin using single-and multi-pollutant models, respectively, as well as environmental, meteorological, and daily mortality data of residents in Tianjin between 2018 and 2020.

Results and discussion

Compared with the AQI, the AQHI and CRI-AQHI established herein correlated more closely with the exposure-response relationships of the total mortality effects on residents. For each increase in the interquartile range of the AQHI, CRI-AQHI and AQI, the total daily mortality rates increased by 2.06, 1.69 and 0.62%, respectively. The AQHI and CRI-AQHI predicted daily mortality rate of residents more effectively than the AQI, and the correlations of AQHI and CRI-AQHI with health were similar. Our AQHI of Tianjin was used to establish specific (S)-AQHIs for different disease groups. The results showed that all measured air pollutants had the greatest impact on the health of persons with chronic respiratory diseases, followed by lung cancer, and cardiovascular and cerebrovascular diseases. The AQHI of Tianjin established in this study was accurate and dependable for assessing short-term health risks of air pollution in Tianjin, and the established S-AQHI can be used to separately assess health risks among different disease groups.

Disease specific air quality health index (AQHI) for spatiotemporal health risk assessment of multi-air pollutants

While significant reductions in certain air pollutant concentrations did not induce obvious mitigations of health risks, a shift from air quality management to health risk prevention and control might be necessary to protect public health. This study thus constructed an Air Quality Health Index (AQHI) for respiratory (Res-AQHI), cardiovascular (Car-AQHI), and allergic (Aller-AQHI) risk groups using mixed exposure under multi-air pollutants and portrayed their distribution and variation at multiple spatiotemporal scales using spatial analysis in GIS with the medical big data and air pollution remote sensing data by taking Hunan Province in China as a case. Results showed that the AQHIs constructed for specific health-risk groups could better express their risks than common AQHI and AQI. Moreover, based on the spatiotemporal association of health and environmental information, the allergic risk group in Hunan provided the highest health risk mainly affected by O₃. The following cardiovascular and respiratory risk groups can be significantly attributed to NO₂. Moreover, the spatiotemporal heterogeneity of AQHIs within regions was also evident. On the annual scale, the population in the air health risk hotspots for respiratory and cardiovascular risk decreased, while allergic risks increased. Meanwhile, on seasonal scale, the hotspots for respiratory and cardiovascular risks expanded significantly in winter while completely disappearing for allergic risk. These findings suggest that disease specific AQHIs effectively disclose the health effects of multi-air pollutants and their subsequently varied spatiotemporal distribution patterns.

The establishment of Air Quality Health Index in China: A comparative analysis of methodological approaches

BACKGROUND

The Air Quality Index (AQI) has been criticized because it does not adequately account for the health effect of multi-pollutants. Although the developed Air Quality Health Index (AQHI) is a more effective communication tool, little is known about the best method to construct AQHI on long time and large spatial scales.

OBJECTIVES

To further evaluate the validity of existing approaches to the establishment of AQHI on both long time and larger spatial scales.

METHODS

By introducing 3 approaches addressing multi-pollutant exposures: cumulative risk index (CRI), supervised principal component analysis (SPCA), and Bayesian multi-pollutants weighted model (BMP), we constructed CRI-AQHI, SPCA-AQHI, BMP-AQHI and standard-AQHI on cardiovascular mortality in China from 2015 to 2019 at both the national and geographic regional levels. We further assessed the performance of the four methods in estimating the joint effect of multi-pollutants by simulations under various scenarios of pollution effect.

RESULTS

The results of national China showed that the BMP-AQHI improved the goodness of fit of the standard-AQHI by 108.24%, followed by CRI-AQHI (5.02%), and all AQHIs performed better than AQI, consistent with 6 geographic regional results. In addition, the simulation result showed that the BMP method provided stable and relatively accurate estimations of the short-term combined effect of exposure to multi-pollutants.

CONCLUSIONS

AQHI based on BMP could communicate the air pollution risk to the public more effectively than the current AQHI and AQI.

Acute cardiorespiratory response to air quality index in healthy young adults

BACKGROUND

Little is known about the acute health impacts of air quality index (AQI) on cardiorespiratory risk factors.

OBJECTIVES

To assess the short-term links of AQI with cardiorespiratory risk factors in young healthy adults.

METHODS

We performed a longitudinal panel study with 4 repeated visits in 40 healthy young adults in Hefei, Anhui Province, China from August to October 2021. Cardiorespiratory factors included systolic blood pressure (BP), diastolic BP (DBP), mean arterial pressure (MAP) and fractional exhaled nitric oxide (FeNO). We collected hourly AQI data from a nearby air quality monitoring site. Linear mixed-effects model was applied to assess the effects of AQI on BP and FeNO.

RESULTS

The study participants (75.0% females) provided 160 pairs of valid health measurements with average age of 24 years. The mean AQI level was 44.43 during the study period. There were significant positive associations of AQI with three BP parameters and FeNO at different lag periods. For example, an interquartile range increase in AQI (26.54 unit) over lag 0-24 h was associated with increments of 6.69 mmHg (95%CI: 2.95-10.44), 5.71 mmHg (95%CI: 3.30-8.13), 6.04 mmHg (95%CI: 3.46-8.62) and 5.67% (95%CI: 1.05%-16.05%) in SBP, DBP, MAP and FeNO, respectively. The results were robust after controlling for PM₁. We did not find effect modifications by gender, BMI, physical activity, or AQI category level on the associations.

CONCLUSIONS

The current findings on associations of AQI with cardiorespiratory factors might add evidence of the acute adverse cardiorespiratory consequences following air pollution.

Regional demarcation of synergistic control for PM2.5 and ozone pollution in China based on long-term and massive data mining

Implementing an inter-regional synergistic control policy for fine particulate matter (PM_2.5) and ground-level ozone (O₃) could improve regional air quality. However, little is known about the effectiveness and accuracy of synergistic control region delineation. This study aimed to construct a network model and apply it to a case study of regional delineation in China at different scales to quantify the interactions between regions. Firstly, the Cumulative Risk Index (CRI) was proposed and quantified from a health risk perspective based on the daily mean PM_2.5 and daily maximum 8-h average O₃ concentrations from 2015 to 2020 in China. Then, the complex network topology parameters were introduced to determine the optimal threshold for different network constructions, and the Girvan-Newman (GN) algorithm was used to divide the network into independent regions. Results showed that the correlation between cities is more robust than that between provinces. There are four-seven major provincial-scale regions with strong synchronicity in CRI, suggesting that PM_2.5 and O₃ synergistic control policies shall be implemented jointly within these demarcated regions. Moreover, urban-scale CRI network analysis indicated that the existing key control areas (2 + 26 cities) need to be expanded to 40-50 cities and refined into seven independent urban regions. Meanwhile, the Fen-Wei Plain can be focused on six cities: Xi'an, Baoji, Xianyang, Weinan, Yuncheng, and Tongchuan. This study could improve our understanding of the synergistic control regions for PM_2.5 and O₃ pollution, and the results could be used to develop joint control policies for both pollutants.

Ambient air pollution and non-communicable respiratory illness in sub-Saharan Africa: a systematic review of the literature

INTRODUCTION

Aerosol pollutants are known to raise the risk of development of non-communicable respiratory diseases (NCRDs) such as asthma, chronic bronchitis, chronic obstructive pulmonary disease, and allergic rhinitis. Sub-Saharan Africa's rapid pace of urbanization, economic expansion, and population growth raise concerns of increasing incidence of NCRDs. This research characterizes the state of research on pollution and NCRDs in the 46 countries of Sub-Saharan Africa (SSA). This research systematically reviewed the literature on studies of asthma; chronic bronchitis; allergic rhinitis; and air pollutants such as particulate matter, ozone, NOx, and sulfuric oxide.

METHODS

We searched three major databases (PubMed, Web of Science, and Scopus) using the key words "asthma", "chronic bronchitis", "allergic rhinitis", and "COPD" with "carbon monoxide (CO)", "sulfuric oxide (SO)", "ozone (O3)", "nitrogen dioxide (NO2)", and "particulate matter (PM)", restricting the search to the 46 countries that comprise SSA. Only papers published in scholarly journals with a defined health outcome in individuals and which tested associations with explicitly measured or modelled air exposures were considered for inclusion. All candidate papers were entered into a database for review.

RESULTS

We found a total of 362 unique research papers in the initial search of the three databases. Among these, 14 met the inclusion criteria. These papers comprised studies from just five countries. Nine papers were from South Africa; two from Malawi; and one each from Ghana, Namibia, and Nigeria. Most studies were cross-sectional. Exposures to ambient air pollutants were measured using spectrometry and chromatography. Some studies created composite measures of air pollution using a range of data layers. NCRD outcomes were measured by self-reported health status and measures of lung function (spirometry). Populations of interest were primarily schoolchildren, though a few studies focused on secondary school students and adults.

CONCLUSIONS

The paucity of research on NCRDs and ambient air pollutant exposures is pronounced within the African continent. While capacity to measure air quality in SSA is high, studies targeting NCRDs should work to draw attention to questions of outdoor air pollution and health. As the climate changes and SSA economies expand and countries urbanize, these questions will become increasingly important.

Nitrogen dioxide component of air pollution increases pulmonary congestion assessed by lung ultrasound in patients with chronic coronary syndromes

Pulmonary congestion is an intermediate biomarker and long-term predictor of acute decompensated heart failure.To evaluate the effects of air pollution on pulmonary congestion assessed by lung ultrasound.In a single-center, prospective, observational study design, we enrolled 1292 consecutive patients with chronic coronary syndromes referred for clinically indicated ABCDE-SE, with dipyridamole (n = 1207), dobutamine (n = 84), or treadmill exercise (n = 1). Pulmonary congestion was evaluated with lung ultrasound and a 4-site simplified scan. Same day values of 4 pollutants were obtained on the morning of testing (average of 6 h) from publicly available data sets of the regional authority of environmental protection. Assessment of air pollution included fine (< 2.5 µm diameter) and coarse (< 10 µm) particulate matter (PM), ozone and nitrogen dioxide (NO₂).NO₂ concentration was weakly correlated with rest (r = .089; p = 0.001) and peak stress B-lines (r = .099; p < 0.001). A multivariable logistic regression analysis, NO₂ values above the median (23.1 µg/m³) independently predicted stress B-lines with odds ratio = 1.480 (95% CI 1.118-1.958) together with age, hypertension, diabetes, and reduced (< 50%) ejection fraction. PM_2.5 values were higher in 249 patients with compared to those without B-lines (median and IQR, 22.0 [9.1-23.5] vs 17.6 [8.6-22.2] µg/m³, p < 0.001). No other pollutant correlated with other (A-C-D-E) SE steps.Higher concentration of NO₂ is associated with more pulmonary congestion mirrored by B-lines at lung ultrasound. Local inflammation mediated by NO₂ well within legally allowed limits may increase the permeability of the alveolar-capillary barrier and therefore pulmonary congestion in susceptible subjects.ClinicalTrials.gov Identifier: NCT030.49995.

Improved morbidity-based air quality health index development using Bayesian multi-pollutant weighted model

BACKGROUND

The widely used Air Quality Index (AQI) has been criticized due to its inaccuracy, leading to the development of the air quality health index (AQHI), an improvement on the AQI. However, there is currently no consensus on the most appropriate construction strategy for the AQHI.

OBJECTIVES

In this study, we aimed to evaluate the utility of AQHIs constructed by different models and health outcomes, and determine a better strategy.

METHODS

Based on the daily time-series outpatient visits and hospital admissions from 299 hospitals (January 2016-December 2018), and mortality (January 2017-December 2019) in Guangzhou, China, we utilized cumulative risk index (CRI) method, Bayesian multi-pollutant weighted (BMW) model and standard method to construct AQHIs for different health outcomes. The effectiveness of AQHIs constructed by different strategies was evaluated by a two-stage validation analysis and examined their exposure-response relationships with the cause-specific morbidity and mortality.

RESULTS

Validation by different models showed that AQHI constructed with the BMW model (BMW-AQHI) had the strongest association with the health outcome either in the total population or subpopulation among air quality indexes, followed by AQHI constructed with the CRI method (CRI-AQHI), then common AQHI and AQI. Further validation by different health outcomes showed that AQHI constructed with the risk of outpatient visits generally exhibited the highest utility in presenting mortality and morbidity, followed by AQHI constructed with the risk of hospitalizations, then mortality-based AQHI and AQI. The contributions of NO₂ and O₃ to the final AQHI were prominent, while the contribution of SO₂ and PM_2.5 were relatively small.

CONCLUSIONS

The BMW model is likely to be more effective for AQHI construction than CRI and standard methods. Based on the BMW model, the AQHI constructed with the outpatient data may be more effective in presenting short-term health risks associated with the co-exposure to air pollutants than the mortality-based AQHI and existing AQIs.

Impact of Air Pollution on Residents' Medical Expenses: A Study Based on the Survey Data of 122 Cities in China

Background: With the development of the social economy, air pollution has resulted in increased social costs. Medical costs and health issues due to air pollution are important aspects of environmental governance in various countries.

Methods: This study uses daily air pollution monitoring data from 122 cities in China to empirically investigate the impact of air pollution on residents' medical expenses using the Heckman two-stage and instrumental variable methods, matching data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) survey.

Results: The study found that poor air quality, measured by the air quality index (AQI), significantly increased the probability of chronic lung disease, heart disease, and self-rated poor health. Additionally, the AQI (with an effect of 4.51%) significantly impacted health-seeking behavior and medical expenses. The medical expenditure effects of mild, moderate, severe, and serious pollution days were 3.27, 7.21, 8.62, and 42.66%, respectively.

Conclusion: In the long run, residents' health in areas with a higher air pollution index, indicating poor air quality, is negatively impacted. The more extreme the pollution, the higher the probability of residents' medical treatment and the subsequent increase in medical expenses. Group and regional heterogeneity also play a role in the impact of air pollution on medical expenses. Compared with the existing literature, this study is based on individuals aged 15 years and above and produces reliable research conclusions.

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.