Construction of AQHI based on the exposure relationship between air pollution and YLL in northern China

The combined effect of air pollution and non-optimal temperature on mortality in Shandong Province, China: establishment of air health index

PURPOSE

The air health index (AHI) captures the combined effects of air pollution and non-optimal temperatures and helps assess the atmospheric environment's overall health risk. Shandong Province is a crucial industrial base in China, and the health effects of air pollution and non-optimal temperature cannot be ignored. To construct an AHI for Shandong Province and assess the district-level mortality burden due to AHI in the study area.

METHODS

Daily district-specific mortality, meteorological, and air pollution data over 2013-2018 were collected in Shandong Province, China. The AHI construction eventually incorporated PM2.5 and NO2, O3, and non-optimal temperatures. Attributable fraction (AF) and attributable number (AN) were used to estimate the district-specific mortality burden attributable to AHI.

RESULTS

The average AHI value observed in Shandong Province was 6. Our research revealed a positive association between the total AHI and total mortality, with an overall trend of a slow increase followed by a rapid increase. The exposure-response curves, when stratified by gender, age, and cause of death, were approximately consistent with the overall trend. The provincial attributable fraction (AF) was 5.31% (95% CI 4.58%, 5.91%), and the attributable number (AN) was 188,246 (95% CI 162,396, 209,533). Overall, higher ANs mainly appeared in the southwestern area, while higher values of AF were observed in the central-eastern and central-northern areas.

CONCLUSIONS

The air health index performs well in predicting death burden and can convey health risks related to exposure to the ambient environment to the public.

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.

Prenatal Exposure to Air Pollution and Pre-Labor Rupture of Membranes in a Prospective Cohort Study: The Role of Maternal Hemoglobin and Iron Supplementation

BACKGROUND

Exposure to air pollution in prenatal period is associated with prelabor rupture of membranes (PROM). However, the sensitive exposure time windows and the possible biological mechanisms underlying this association remain unclear.

OBJECTIVE

We aimed to identify the sensitive time windows of exposure to air pollution for PROM risk. Further, we examined whether maternal hemoglobin levels mediate the association between exposure to air pollution and PROM, as well as investigated the potential effect of iron supplementation on this association.

METHOD

From 2015 to 2021, 6,824 mother-newborn pairs were enrolled in the study from three hospitals in Hefei, China. We obtained air pollutant data [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5), PM with aerodynamic diameter ≤10μm (PM10), sulfur dioxide (SO2), and carbon monoxide (CO)] from the Hefei City Ecology and Environment Bureau. Information on maternal hemoglobin levels, gestational anemia, iron supplementation, and PROM was obtained from medical records. Logistic regression models with distributed lags were used to identify the sensitive time window for the effect of prenatal exposure to air pollutant on PROM. Mediation analysis estimated the mediated effect of maternal hemoglobin in the third trimester, linking prenatal air pollution with PROM. Stratified analysis was used to investigate the potential effect of iron supplementation on PROM risk.

RESULTS

We found significant association between prenatal exposure to air pollution and increased PROM risk after adjusting for confounders, and the critical exposure windows of PM2.5, PM10, SO2 and CO were the 21th to 24th weeks of pregnancy. Every 10-μg/m3 increase in PM2.5 and PM10, 5-μg/m3 increase in SO2, and 0.1-mg/m3 increase in CO was associated with low maternal hemoglobin levels [-0.94g/L (95% confidence interval (CI): -1.15, -0.73), -1.31g/L (95% CI: -1.55, -1.07), -2.96g/L (95% CI: -3.32, -2.61), and -1.11g/L (95% CI: -1.31, -0.92), respectively] in the third trimester. The proportion of the association between air pollution and PROM risk mediated by hemoglobin levels was 20.61% [average mediation effect (95% CI): 0.02 (0.01, 0.05); average direct effect (95%): 0.08 (0.02, 0.14)]. The PROM risk associated with exposure to low-medium air pollution could be attenuated by maternal iron supplementation in women with gestational anemia.

CONCLUSIONS

Prenatal exposure to air pollution, especially in the 21st to 24th weeks of pregnancy, is associated with PROM risk, which is partly mediated by maternal hemoglobin levels. Iron supplementation in anemia pregnancies may have protective effects against PROM risk associated with exposure to low-medium air pollution. https://doi.org/10.1289/EHP11134.

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.

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.

The life expectancy benefits on respiratory diseases gained by reducing the daily concentration of particulate matter to attain different air quality standard targets: findings from a 5-year time-series study in Tianjin, China

The short-term effects of particulate matter (PM) on years of life lost (YLL) have been confirmed by several studies; however, little attention has been paid to the effects of PM on life expectancy from respiratory diseases (RD), especially at the city level. A generalized additive model (GAM) was used to assess the associations between daily PM and YLL of the RD and chronic obstructive pulmonary diseases (COPD) in Tianjin from 2015 to 2019. Then the daily avoidable YLL, attributable fraction, and potential life expectancy benefits (PLEB) of RD and COPD by reducing the daily concentration of PM_2.5 and PM₁₀ to attain the Chinese and World Health Organization (WHO) air quality standard targets were estimated during 2015-2019. The stratified analyses by gender were also conducted. A total of 18279 RD and 7767 COPD deaths were included in this study. Per 10 μg/m³ increments in PM_2.5 and PM₁₀ at lag1 were significantly associated with 0.83(95%CI: 0.11, 1.55) years and 0.57(95%CI: 0.06, 1.08) years increasing in YLL of total RD and 0.48 (95%CI: 0.07, 0.90) years and 0.30 (95%CI: 0.01, 0.60) years increasing in YLL of total COPD. If the daily PM_2.5 and PM₁₀ concentrations decreased to attain the Chinese and WHO air quality standard targets, 0.06-0.39 years PLEB due to total RD and 0.08-0.53 years PLEB due to COPD could be gained. And more PLEB on RD could be gained by decreasing the daily PM concentrations to attain the WHO air quality standard targets than that of attaining the Chinese air quality standard targets. These evidence-based findings emphasize the importance of the Chinese PM-related guideline updating and may have implications for PM-related policy-making.

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.

Fresh Air–Natural Microclimate Comfort Index: A New Tourism Climate Index Applied in Chinese Scenic Spots

Severe air pollution in China has caused significant tourism transformation for pursuing fresh air in microclimate tourism markets. Contemporary practices simply measure the air freshness of destinations and scenic spots using a single index, i.e., primarily negative oxygen ions (O2−). This index cannot comprehensively reveal scenic spots’ air freshness degree and determine the dynamic interactions between air freshness and scenic spots’ tourism development, thus inducing an illusion of air freshness for the target scenic spots. Meanwhile, the current fresh air index primarily ignores connections with the microclimate index of scenic spots and cannot provide a multidimensional index for scenic spots to take advantage of both air and microclimate resources for diverse tourism products and service production. Therefore, this study proposes a multidimensional index, the fresh air–natural microclimate comfort index (FAI-NMCI), connecting the fresh air index with the natural microclimate comfort index of scenic spots together from transdisciplinary and multidisciplinary perspectives. This study utilizes FAI-NMCI to measure four scenic spots of Fujian Province, and reveals in-depth results of scenic spots’ air freshness and natural microclimate comfort degree together. The results demonstrate that the four scenic spots in Fujian province of China had different levels of air freshness degree and natural microclimate comfort degree in 2018. The natural scenic spots were mostly distributed in Healing Fresh, Very Fresh, and Super Fresh levels of FAI with the most comfortable and comfortable levels of NMCI. The cultural scenic spots were mostly distributed in Relatively Fresh and Healing Fresh levels of FAI with the most comfortable and comfortable levels of NMCI. Meanwhile, the FAI-NMCI of natural and cultural scenic spots also had significant differences within 24 Jieqi, which will promote dynamic and creative utilization of those resources in microclimate tourism development.

Combining machine learning models through multiple data division methods for PM2.5 forecasting in Northern Xinjiang, China

In this study, daily average PM_2.5 forecasting models were developed and applied in the Northern Xinjiang, China, through combining the back propagation artificial neural network (BPANN) and multiple linear regression (MLR) with another BPANN model. The meteorological (daily average precipitation, pressure, relative humidity, temperature, and wind speed, daily maximum wind speed and sunshine hours on the same day) and air pollutant data (daily PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃ concentrations on the previous day) in January and August of each year from 2015 to 2019 were used as candidate inputs. The optimal member and combining models were evaluated through the leave-one-out cross-validation (LOOCV), fivefold cross-validation, and hold-out methods. Twelve member models with optimal or sub-optimal performance were further used to develop the combining models. The performances of the BPANN and MLR member models were different using three data division methods. The models were evaluated more comprehensively through the LOOCV. The performances of the combining models were generally better than the member models. For both member and combining models, the PM_2.5 forecasting model performance in August was generally better than in January. The correlation coefficient (R) for the validation set of the optimal combination model was about 0.87 in January and 0.946 in August. These results showed that combining linear and nonlinear models through multiple data division methods would be an effective tool to forecast PM_2.5 concentrations.

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

The most common currently used air quality risk communication tool, the Air Quality Index (AQI), has been criticized. As a result, Canada proposed the Air Quality Health Index (AQHI) to communicate the health risks of multiple pollutants. However, the AQHI is calculated by directly summing the excess risks from single-pollutant models, which may overestimate the effects of the pollutants. To solve this problem, we introduced two methods for estimating the joint effects of multiple pollutants: the cumulative risk index (CRI) and supervised principal component analysis (SPCA). Based on three methods, i.e., the standard, CRI and SPCA methods, we constructed three types of AQHIs and compared their validity to select the best communication tool. Our results showed that compared with the AQI, all three AQHIs had a linear relationship with mortality. In addition, the CRI-AQHI had the best goodness of fit and captured the overall health risk of pollution mixtures most robustly among various cause-specific mortalities when identifying health risks. Our study indicated that the CRI-AQHI may have the potential to be a better alternative to the standard AQHI in communicating air pollution-related health risks to the public.

Development of a Health-Based Index to Identify the Association between Air Pollution and Health Effects in Mexico City

Health risks from air pollution continue to be a major concern for residents in Mexico City. These health burdens could be partially alleviated through individual avoidance behavior if accurate information regarding the daily health risks of multiple pollutants became available. A split sample approach was used in this study to create and validate a multi-pollutant, health-based air quality index. Poisson generalized linear models were used to assess the impacts of ambient air pollution (i.e., fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ground-level ozone (O3)) on a total of 610,982 daily emergency department (ED) visits for respiratory disease obtained from 40 facilities in the metropolitan area of Mexico City from 2010 to 2015. Increased risk of respiratory ED visits was observed for interquartile increases in the 4-day average concentrations of PM2.5 (Risk Ratio (RR) 1.03, 95% CI 1.01–1.04), O3 (RR 1.03, 95% CI 1.01–1.05), and to a lesser extent NO2 (RR 1.01, 95% CI 0.99–1.02). An additive, multi-pollutant index was created using coefficients for these three pollutants. Positive associations of index values with daily respiratory ED visits was observed among children (ages 2–17) and adults (ages 18+). The use of previously unavailable daily health records enabled an assessment of short-term ambient air pollution concentrations on respiratory morbidity in Mexico City and the creation of a health-based air quality index, which is now currently in use in Mexico City.