Heatwave events and concurrent ozone concentrations between 2006 and 2022 at two sites in southern and northern Spain.

Hotter-than-usual days are becoming more common, such that heat waves are expected to increase in intensity, frequency, duration, and spatial extent in Spain. Within this framework, this paper looks at the combined effects of extreme temperatures and air pollution in two cities in Spain, Córdoba and Valladolid, over the period 2006-2022. Synoptic patterns and air mass movement were analysed during the eleven coincident heat waves at both locations in order to study what impact orography and local meteorology have on ozone concentrations. Weak flow conditions were the most frequent synoptic pattern in the Iberian Peninsula during heat waves. Moreover, west and local circulations characterised the main air trajectories at low levels (500 m agl), while southwest maritime advections and African air mass transport were more frequent at higher levels (1500 and 3000 m agl) in Córdoba and Valladolid, respectively. On average, maximum ozone values were higher in Córdoba (105.1 µg m-3) than in Valladolid (80 µg m-3) and were strongly correlated with extreme temperatures at both locations (r up to 0.8, p-value < 0.05). Mean temperature in Córdoba was 31.9 °C, with the maximum value reaching 43.7 °C, while temperatures in Valladolid were lower (28.3 °C and 37.3 °C, respectively). Calculation and assessment of some indices helped to understand the impact of extreme events. Caution actions based on the Heat Index characterised heat wave periods. Moderate risk was the general Air Quality Health Index feature recorded and reached a very high risk of unhealthy air quality in the June 2022 event in Córdoba.

The construction and validity assessment of the respiratory air quality health index (AQHI) based on the analytic hierarchy process in Tianjin, China.

BACKGROUND: Air quality health index (AQHI), as a developed air quality risk communication tool, has been proved to be more accurate in predicting air quality related health risks than air quality index (AQI) by previous studies. However, the standard method to construct AQHI is summing the excess risks of single-pollutant models directly, which may ignore the joint effect of air pollutant mixtures. METHODS: In this study, a new method which could solve the aforementioned problem, Analytic hierarchy process (AHP), was introduced. Based on this method, we constructed the respiratory health related AQHI using years of life lost (YLL) as indicator of health outcome and compared its validity with AQI. RESULTS: There was a correlation between daily AQI and AQHI in 2019 (R2 = 0.830, P < 0.01), and the chi-square test between the two excellent rates showed a statistically significant difference (χ2 = 4.156, P < 0.05). Both AQI and AQHI were correlated with the daily respiratory YLL (P < 0.01), however, the coefficient of AQHI was larger than those of AQI. CONCLUSIONS: This study indicated that compared with AQI, the constructed AQHI based on AHP may predict the health risk of air pollution more effectively. AHP may become a new method to construct AQHI which needs to be proved by taking into consideration by more studies.

Developing and validating intracity spatiotemporal air quality health index in eastern China.

BACKGROUND: Recently pilot published city-level air quality health index (AQHI) provides a useful tool for communicating short-term health risks of ambient air pollution, but fails to account for intracity spatial heterogeneity in exposure and associated population health impacts. This study aims to develop the intracity spatiotemporal AQHI (ST-AQHI) via refined air pollution-related health risk assessments. METHODS: A three-stage analysis was conducted through integrating province-wide death surveillance data and high-resolution gridded estimates of air pollution and climate factors spanning 2016-2019 in Jiangsu Province, eastern China. First, an individual-level case-crossover design was employed to quantify the short-term risk of nonaccidental mortality associated with residential exposure to individual pollutant (i.e., PM2.5, NO2, O3, and SO2). Second, we accumulated and scaled the excess risks arising from multiple pollutants to formulate daily gridded ST-AQHI estimates at 0.1° × 0.1°, dividing exposure-related risks into low (0-3), moderate (4-6), high (7-9), and extreme high (10+) levels. Finally, the effectiveness of ST-AQHI as composite risk communication was validated through checking the dose-response associations of individual ST-AQHI exposure with deaths from nonaccidental and major cardiopulmonary causes via repeating case-crossover analyses. RESULTS: We analyzed a total of 1,905,209 nonaccidental death cases, comprising 785,567 from circulatory diseases and 247,336 from respiratory diseases. In the first-stage analysis, for each 10-µg/m3 rise in PM2.5, NO2, O3, and SO2 exposure at lag-01 day, population risk of nonaccidental death was increased by 0.8% (95% confidence interval: 0.7%, 0.9%), 1.9% (1.7%, 2.0%), 0.4% (0.3%, 0.5%), and 4.1% (3.7%, 4.5%), respectively. Spatiotemporal distribution of ST-AQHI exhibited a consistent declining trend throughout the study period (2016-2019), with annual average ST-AQHI decreasing from 5.2 ± 1.3 to 4.0 ± 1.0 and high-risk days dropping from 15.8% (58 days) to 1.6% (6 days). Exposure associated health risks showed great intracity- and between-city heterogeneities. In the validation analysis, ST-AQHI demonstrated approximately linear, threshold-free associations with multiple death events from nonaccidental and major cardiopulmonary causes, suggesting excellent performance in predicting exposure-related health risks. Specifically, each 1-unit rise in ST-AQHI was significantly associated with an excess risk of 2.0% (1.8%, 2.1%) for nonaccidental mortality, 2.3% (2.1%, 2.6%) for overall circulatory mortality, and 2.7% (2.3%, 3.1%) for overall respiratory mortality, as well as 1.7%-3.0% for major cardiopulmonary sub-causes. CONCLUSIONS: ST-AQHI developed in this study performed well in predicting intracity spatiotemporal heterogeneity of death risks related to multiple air pollutants, and may hold significant practical importance in communicating air pollution-related health risks to the public at the community scales.

A New Global Air Quality Health Index Based on the WHO Air Quality Guideline Values With Application in Cape Town.

Objectives: This study developed an Air Quality Health Index (AQHI) based on global scientific evidence and applied it to data from Cape Town, South Africa. Methods: Effect estimates from two global systematic reviews and meta-analyses were used to derive the excess risk (ER) for PM2.5, PM10, NO2, SO2 and O3. Single pollutant AQHIs were developed and scaled using the ERs at the WHO 2021 long-term Air Quality Guideline (AQG) values to define the upper level of the "low risk" range. An overall daily AQHI was defined as weighted average of the single AQHIs. Results: Between 2006 and 2015, 87% of the days posed "moderate to high risk" to Cape Town's population, mainly due to PM10 and NO2 levels. The seasonal pattern of air quality shows "high risk" occurring mostly during the colder months of July-September. Conclusion: The AQHI, with its reference to the WHO 2021 long-term AQG provides a global application and can assist countries in communicating risks in relation to their daily air quality.

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.

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.

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.

Long-term variation and evaluation of air quality across Hong Kong.

Study of Air Quality Objectives (AQOs) and long-term changes of air pollution plays a decisive role in formulating and refining pollution control strategies. In this study, 10-year variations of six major air pollutants were analyzed at seven monitoring sites in Hong Kong. The continuous decrease of annual averaged concentrations of NO2, SO2, CO, PM2.5 and PM10 and numbers of days with severe pollution conditions validated the efficiency of the series of air pollution control schemes implemented by the Hong Kong government. However, there is still a big gap to meet the ultimate targets described by the World Health Organization. Besides, the concentration of O3 at roadside and urban stations increased by 135% ± 25% and 37% ± 18% from 2011 to 2020, respectively, meanwhile the highest 8 hr averaged O3 concentration was observed as 294 µg/m3 at background station in 2020, which pointed out the increasing ozone pollution in Hong Kong. There was a great decrease in the annual times of air quality health index (AQHI) laying in "high", "very high" and "serious" categories from 2011 to 2020 with the decrease rate of 89.70%, 91.30% and 89.74% at roadside stations, and 79.03%, 95.98% and 72.73% at urban stations, respectively. Nevertheless, the number of days categorized as "high" or above at roadside station was twice more than that in the urban station during the past ten years. Thus, more policies and attentions should be given to the roadside air quality and its adverse health effect to pedestrians on street.

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.

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 PM2.5 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.

Discrete-Time Markov Chain Modelling of the Ontario Air Quality Health Index.

The Air Quality Health Index (AQHI) is an aggregate indicator of air pollution used to communicate to Canadians the health impact of short-term exposure to current air pollutant levels. Understanding the stochastic behaviour of the AQHI can aid public health officials in predicting air pollution levels, determining the likelihood and duration of air quality advisories, and planning for increased strain on the health care system during periods of higher air pollution. Previous research has applied discrete-time Markov chains to investigate stochastic behaviour of air pollution indices but only in a handful of regions and none with the same climatic characteristics as Canadian regions. In this study, we investigated the stochastic behaviour of AQHI risk categories in Ontario (34 air monitoring stations) for 5 years from 2015 to 2019. We employed discrete-time Markov chains using three of the AQHI risk categories (Low Risk, Moderate Risk, High Risk) as states to determine (1) the transition probabilities between these states, (2) the long-run proportion of time spent in each state, and (3) the mean persistence time of each state. These results were then used to assess spatial trends in the stochastic behaviour of AQHI risk categories and the likelihood and duration of air quality advisories. Overall, the air quality (as characterised by the AQHI) in Ontario tends to decrease as population density increases. Urban areas spent a greater proportion of time in higher risk categories, and tended to remain in the higher risk categories for longer before transitioning.

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.

Increased Weekly Mean PM2.5, and NO2 Are Associated With Increased Proportions of Lower Airway Granulocytes in Ontario Horses.

Ambient pollution is associated with the development and exacerbation of human asthma, but whether air pollution exposure is associated with lower airway inflammation in horses has not been fully evaluated. The Air Quality Health Index (AQHI) is an online tool used by asthmatic Ontarians to modify their outdoor activity when ambient pollution is high. A single AQHI value, falling on a scale from 1 to 10+, is calculated from measurements of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3). Increased AQHI values predict an increased risk for presenting to a health care provider for assessment of asthma exacerbation, with a time lag of 0-9 days after an increase. Whether ambient air pollution is a risk factor for identifying increased lower airway inflammatory cells on cytologic evaluation of bronchoalveolar lavage fluid (BALF) of horses has not yet been explored. To investigate this relationship, case data including BALF cytology preparations from horses across southern Ontario, Canada, were retrieved from the Guelph Animal Health Laboratory's archives. Spanning the years 2007-2017, 154 cases were identified within a 41- by 30-km area surrounding the cities of Guelph and Kitchener. In 78 of 154 cases, cytologic reevaluation identified increased proportions of one or a combination of BALF neutrophils (mean 5%, range 0-15%), eosinophils (mean 2%, range 0-31%), and mast cells (mean 4%, range 0-10%). To assess the effect of lagged pollutant and temperature exposures in these 78 cases, weekly mean values of AQHI, PM2.5, NO2, O3, and temperature were recorded for the 4 weeks prior to the date of the horse's presentation for respiratory tract evaluation. The relationship between ambient exposures and increased proportions of lower airway granulocytes was evaluated using a case-crossover design. Single unit increases in 2-, and 3-week lagged weekly mean PM2.5 and NO2, were associated, respectively, with an 11% (p = 0.04, 95% confidence interval, CI = 1.01-1.22), and 24% (p = 0.03, 95% CI = 1.08-1.43) greater risk of identifying increased lower airway granulocytes. These findings suggest that exposure to increased ambient pollutants is associated with lower airway inflammation in Guelph and Kitchener area horses.

The establishment of National Air Quality Health Index in China.

BACKGROUND: A new Air Quality Health Index (AQHI) was developed in Canada or several single cities as a promising health risk communication tool. OBJECTIVES: To construct a national AQHI in China and compare its validity in predicting daily mortality risk with the existing Air Quality Index (AQI). METHODS: We established the AQHI as the sum of excess total mortality risks associated with multiple air pollutants in 272 representative Chinese cities from 2013 to 2015 (termed as "total AQHI"). The mortality risks per unit change of air pollutant concentrations were determined according to a time-series analysis in each city. Separate AQHIs were established for subgroups classified by age and sex and for main cardiopulmonary diseases (termed as "specific AQHIs"). For validation, AQHIs and AQI were established using the data of 2015 (N = 272) and compared their associations with daily mortality using the data of 2013-2014 (N = 144). RESULTS: The concentration-response coefficients of fine particulate matter, nitrogen dioxide and ozone were adopted in constructing AQHI. There were almost linear exposure-response relationships between AQHIs and daily mortality. The total AQHI and specific AQHIs had very similar associations with daily mortality. AQHI and AQI showed similar associations with daily cause-specific mortality in terms of average magnitude, numbers of cities of positive associations and model fit statistics. CONCLUSIONS: AQHI may have comparable performance with AQI in communicating acute health risks of air pollution in China. There seems no need to establish specific AQHIs for different age groups, gender and causes of deaths.

Improved risk communications with a Bayesian multipollutant Air Quality Health Index.

Establishing an optimal indicator to communicate health risks of multiple air pollutants to public is much important. The Air Quality Health Index (AQHI) has been developed in many countries as a communication tool of multiple air pollutants related health risks. However, the current AQHI is based on the sum of the excess health risks which are typically derived from the single-pollutant statistical models. Such a strategy may overestimate the joint effect of multiple pollutants. We proposed an improved strategy to construct the AQHI based on a Bayesian multipollutant weighted model. Using this strategy, two improved indices - Bayesian multipollutant AQHI (BMP-AQHI) and Bayesian multipollutant AQHI with seasonal specificity (SBMP-AQHI) were calculated to present the multiple pollutants related health risks to the cardiovascular system based on data collected in Chengdu, China during 2013 to 2018. The two improved indices were compared to current Air Quality Index (AQI) and AQHI to evaluate the effectiveness of the improved indices in characterizing multipollutant health risks. The AQI risk classification suggested much smaller health risks than AQHIs. Among three AQHI types, the BMP-AQHI and SBMP-AQHI suggested slightly lower health risks to the cardiovascular system than the current AQHI. In the evaluation analysis, the SBMP-AQHI had the strongest association with the mortality of cardiovascular disease (CVD) (2.66%; 95%CI, 1.57%, 3.76%). In the subgroup analysis, an interquartile increase (IQR) of the SBMP-AQHI was associated with 3.21% (95%CI, 2.06%, 4.38%), 1.34% (95%CI, -0.13%, 2.82%), and 4.20% (95%CI, 2.59%, 5.84%) increases for CVD mortality in the elderly, male, and female subgroups, respectively. The study shows that the improved AQHIs can communicate the health information of multiple air pollutants more efficiently. The study also indicates the necessity to consider seasonal specificity in the construction of the AQHI.

Autonomous monitoring, analysis, and countering of air pollution using environmental drones.

The effect of air pollution on the environment, economic and health of the people in the affected countries cannot be overemphasized. This paper investigates large scale air pollution elimination to remove pollutants that are already in existence in the environment. This method involves the use of Environmental Drones (E-drones) to autonomously monitor the air quality at a specific location. The E-drone flies up to a predetermined height (Ealtitude) every hour, measures the air pollutants at that location, implements on-board pollution abatement solutions for pollutants above the recommended threshold, and then flies back down to its location on the ground. The advantages of this system is its ability to measure air pollution concentration of CO2, CO, NH3, SO2, PM, O3 and NO2, detect when they are too high, and implement on-board pollution abatement solutions as needed. This system's novelty lies in the fact that it not only detects when there is excessive pollution, but it also automatically deals with and abates the detected air pollution above earth. When multiple E-drones are used in different locations, a custom software generates an Air Quality Health Index (AQHI) map of the region that can be used for present and long-term environmental analysis.

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

The current air quality index (AQI) has been argued for failing to respond to the combined health effects of multiple air pollutants. Thus, it is a challenge to construct a new indicator, air quality health index (AQHI) to comprehensively assess and predict air quality and the health effects caused by air pollution. Here, we have comprehensively considered the relationship between six air pollutants and the total mortality. And we constructed AQHI using the principal component analysis (FCA) by taking into account of the associations between six main air pollutants and YLL in Tianjin, China from 2014 to 2017. Then, we used the K-fold cross-validation method and the method of comparing AQHI with AQI to assess the validity of AQHI, respectively. Two principal components (F1 and F2) were used to construct AQHI; the cumulative contribution rate of variance for them was >70% (53.6% and 16.4%, respectively). With each unit increase of F1, the total daily YLL increased by 18.420 person-years. With each unit increase of F2, the total daily YLL increased by 22.409 person-years. The correlation between the predicted and actual values of total mortality and total YLL of AQHI was 0.742 (P < 0.001) and 0.700 (P < 0.001), respectively. The difference between AQI and AQHI was statistically significant (χ2 = 103.15, P < 0.001). There was a correlation between AQHI and AQI (r = 0.807, P < 0.01), and the grading was also correlated (rs = 0.580, P < 0.01). With an increase of interquartile range (IQR) for AQHI, the daily YLL increased by 32.797 (95% CI: 14.559, 51.034), while for the AQI, the daily YLL increased by 22.367 (95% CI: 6.619, 38.116), which was less than AQHI. These results imply that AQHI can comprehensively consider the impact of various pollutants on disease mortality and YLL, and can comprehensively reflect air quality, which has an important practical significance.

Assessment of the Air Quality Health Index (AQHI) and four alternate AQHI-Plus amendments for wildfire seasons in British Columbia.

OBJECTIVE: Wildfire smoke is an important source of air pollution associated with a range of cardiopulmonary health conditions. The Air Quality Health Index (AQHI) is the most widely used tool in Canada to communicate with the public about air pollution, but it may not adequately reflect health risks from wildfire smoke. The objective of this study was to evaluate the ability of the AQHI and four alternate AQHI-Plus amendments to predict adverse population health effects from wildfire smoke. METHODS: The maximum 1-h values of the AQHI and the four amendments were calculated for each 48-h period of the wildfire seasons from 2010 to 2017 for 32 health units in British Columbia. Generalized Poisson models were used to estimate the association between these values and daily counts of five health outcomes: all-cause mortality; physician visits for all circulatory causes; visits for all respiratory causes, including asthma; asthma-specific visits; and dispensations of salbutamol sulfate (i.e., Ventolin®). Model fit was evaluated with the Akaike information criterion. RESULTS: The AQHI and the four amendments were all associated with all five health outcomes. The AQHI exhibited best fit to the all-cause mortality and circulatory physician visits during all wildfire seasons, while the 1-h PM2.5Only AQHI-Plus exhibited best fit to the asthma-related outcomes during all wildfire seasons. CONCLUSION: Individuals with common respiratory conditions such as asthma and chronic obstructive pulmonary disease are particularly susceptible to wildfire smoke. As such, the 1-h PM2.5Only AQHI-Plus amendment was recommended for communicating about potential health effects of air quality during wildfire seasons in BC.

A Systematic Review of the Short-Term Health Effects of Air Pollution in Persons Living with Coronary Heart Disease.

Persons living with chronic medical conditions (such as coronary artery disease (CAD)) are thought to be at increased risk when exposed to air pollution. This systematic review critically evaluated the short-term health effects of air pollution in persons living with CAD. Original research articles were retrieved systematically through searching electronic databases (e.g., Medical Literature Analysis and Retrieval System Online (MEDLINE)), cross-referencing, and the authors' knowledge. From 2884 individual citations, 26 eligible articles were identified. The majority of the investigations (18 of 22 (82%)) revealed a negative relationship between air pollutants and cardiac function or overall health. Heart rate variability (HRV) was the primary cardiovascular outcome measure, with 10 out of 13 studies reporting at least one index of HRV being significantly affected by air pollutants. However, there was some inconsistency in the relationship between HRV and air pollutants, mediated (at least in part) by the confounding effects of beta-blocker medications. In conclusion, there is strong evidence that air pollution can have adverse effects on cardiovascular function in persons living with CAD. All persons living with CAD should be educated on how to monitor air quality, should recognize the potential risks of excessive exposure to air pollution, and be aware of strategies to mitigate these risks. Persons living with CAD should minimize their exposure to air pollution by limiting outdoor physical activity participation when the forecast air quality health index indicates increased air pollution (i.e., an increased risk).

The construction and validity analysis of AQHI based on mortality risk: A case study in Guangzhou, China.

The current air quality indices have been criticized for not capturing combined health effects of multiple air pollutants. We proposed an Air Quality Health Index (AQHI) based on the air pollution-mortality associations for communicating health risks of air pollution. Time-series studies were conducted to estimate the associations between air pollutants including sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), suspended particulate matter smaller than 2.5 µm in aerodynamic diameter (PM2.5) and mortality in Guangzhou from 2012 to 2015. The sum of excess mortality risk was calculated to construct the AQHI, which was then adjusted to an arbitrary scale. We then assessed the validity of AQHI. An interquartile increase (IQR) of AQHI was associated with 3.61% [95% confidence interval (95% CI), 2.85%-4.37%], 3.73% (95%CI, 2.18%-5.27%) and 4.19% (95%CI, 2.87%-5.52%) increase of mortality, respiratory and cardiovascular hospital admissions, respectively. Compared with the currently used Air Quality Index (AQI), AQHI had higher effects on mortality and morbidity. Our study suggests that AQHI might comprehensively capture the combined effects of air pollution, which make it be a more valid communication tool of air pollution-related health risk.