Current Air Quality Index Research Articles

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.

Improving the current air quality index with new particulate indicators using a robust statistical approach

To convey the severity of ambient air pollution level to the public, air quality index (AQI) is used as a communication tool to reflect the concentrations of individual pollutants on a common scale. However, due to the enhanced air pollution control in recent years, air quality has improved, and the roles of some air pollutant species included in the existing AQI as urban air pollutants have diminished. In this study, we suggest the current AQI should be revised in a way that new air pollution indicators would be considered so that it would better represent the health effects caused by local combustion processes from traffic and residential burning. Based on the air quality data of 2017–2019 in three different sites in Helsinki metropolitan area, we assumed the statistical distributions of the current indicators (NO2 and PM2.5) and the proposed particulate indicators (BC, LDSA and PNC) were related as they have similar sources in urban regions despite the varying correlations between the current and proposed indicators (NO2: r = 0.5–0.85, PM2.5: r = 0.28–0.72). By fitting the data to an optimal distribution function, together with expert opinions, we improved the current Finnish AQI and determined the AQI breakpoints for the proposed indicators where this robust statistical approach is transferrable to other cities. The addition of the three proposed indicators to the current AQI would decrease the number of good air quality hours in all three environments (largest decrease in urban traffic site, ~22 %). The deterioration of air quality class appeared more severe during peak hours in the urban traffic site due to vehicular emission and evenings in the detached housing site where domestic wood combustion often takes place. The introduction of the AQI breakpoints of the three new indicators serve as a first step of improving the current AQI before further air quality guideline levels are updated.

Prophecy of Air Quality using KNN-LSTM

Since the vast majority of the current air quality Index i.e (AQI) foreseeing models zeroed in on forecast of the time series information of a solitary objective observing station, they neglected to consider the connection and shared impact among the air quality checking station destinations and the spatio-transient attributes of air quality. This will prompt a specific one-sidedness during air quality expectation of a specific site. A prototype to predict the AQI for a short period of time was based on the K-nearest algorithm and long short memory was proposed. The air quality index of stations was made into data sets and fed for testing processing of data in the LSTM model whose prediction accuracy was dependent on the time correlation. Long Short-term Memory Neural Network: The Recurrent Neural Network problem which involved disappearing gradient when dealing with long term dependency came to an end with development of Long Short-Term Memory (LSTM) model .The working of this special type of RNN involves adding of additional layer of memory units such that the time series controllable and maintainable by using the 3 controllers to control the more than one memory cells in the memory units. KNN algorithm: When it comes to classification in the machine learning the K nearest neighbour algorithm stands in prominence, the algorithm works by predicting the relationship and distance between the data sets or samples given of different sort..

Assessment of pedestrian exposure and deposition of PM10, PM2.5 and ultrafine particles at an urban roadside: A case study of Xi'an, China

Assessment of pedestrian exposure and lung deposition is critical to better understanding the health risks resulting from exposure to ambient particles, and to develop site-specific risk control countermeasures. In this study, real-time mobile-monitoring campaigns of three different PM size fractions (PM10, PM2.5 and UFP) were performed during morning and evening peak hours in two seasons (summer and winter), along road sections with differing characteristics (commercial and residential) in Xi'an, China. The Multiple-Path Particle Dosimetry model (MPPD) was utilized to quantify the total and regional depositions for both adults and teens of different genders. Observed PM10, PM2.5 and UFP concentrations showed pronounced regional, seasonal, and diurnal variabilities. The total deposition dose was higher in males than in females for all particle-size fractions in both age groups, except for UFPs in the teen group, the total deposition dose was higher in females. As for regional depositions, these were observed to be highest in the head region, compared with the tracheobronchial and pulmonary regions, regardless of a participant's age or gender. PM10 and PM2.5 showed the highest depositions in the head region for all age groups (PM10: 90%; PM2.5: 67%), while UFP was highly deposited in the pulmonary region (66%). The dominant percentage of UFP deposition in the pulmonary regions strengthens the need to incorporate UFP into the current air-quality index.

Air quality and public health risk assessment: A case study in a typical polluted city, North China

To evaluate air quality changes along with public health in Jinan, a typical polluted city of North China, spatial and temporal features of six criteria pollutants and relative health risk were conducted from 2013 to 2017. In general, the concentrations of criteria air pollutants except ozone had obviously decreased. For spatial pattern, the air qualities in north Jinan were influenced by dust, vehicle and industry emission. Health risk evaluation showed people mainly suffered health risk from PM10 and PM2.5. However, O3 had become another noticeable pollutant in recent years. Compared with other cities in China, the citizens in Jinan were more likely to suffer from much higher health risk categories than the current Air Quality Index (AQI) approach, particularly in winter. During severe polluted episodes, secondary formation via heterogeneous reaction would increase public health risk from air pollution. Back trajectories and potential source contribution function (PSCF) analysis identified anthropogenic sources from near provinces particularly to the east and south may aggravate air pollution by air mass transport. In general, the emission controls on VOCs, NO2 and regional joint pollution prevention would be essential to reduce urban air pollution in the next phases.

Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration

Air quality prediction with black-box (BB) modelling is gaining widespread interest in research and industry. This type of data-driven models work generally better in terms of accuracy but are limited to capture physical, chemical and meteorological processes and therefore accountability for interpretation. In this paper, we evaluated different white-box (WB) and BB methods that estimate atmospheric black carbon (BC) concentration by a suite of observations from the same measurement site. This study involves data in the period of 1st January 2017–31st December 2018 from two measurement sites, from a street canyon site in Mäkelänkatu and from an urban background site in Kumpula, in Helsinki, Finland. At the street canyon site, WB models performed (R2 = 0.81–0.87) in a similar way as the BB models did (R2 = 0.86–0.87). The overall performance of the BC concentration estimation methods at the urban background site was much worse probably because of a combination of smaller dynamic variability in the BC values and longer data gaps. However, the difference in WB (R2 = 0.44–0.60) and BB models (R2 = 0.41–0.64) was not significant. Furthermore, the WB models are closer to physics-based models, and it is easier to spot the relative importance of the predictor variable and determine if the model output makes sense. This feature outweighs slightly higher performance of some individual BB models, and inherently the WB models are a better choice due to their transparency in the model architecture. Among all the WB models, IAP and LASSO are recommended due to its flexibility and its efficiency, respectively. Our findings also ascertain the importance of temporal properties in statistical modelling. In the future, the developed BC estimation model could serve as a virtual sensor and complement the current air quality monitoring. Main findingsWhite-box models are preferred over black-box models in estimating black carbon because they are closer to physics-based models, and it is easier to spot the relative importance of the predictor variable. The black carbon model could serve as a virtual sensor integrating into air quality network in support with real measurements, so as to complement the current air quality index.

Differential inflammatory potential of particulate matter (PM) size fractions from imperial valley, CA

Particulate matter (PM) in Imperial Valley originates from a variety of sources such as agriculture, traffic at the border crossing, emissions from the cross-border city of Mexicali, and the drying lakebed of the Salton Sea. Dust storms in Imperial Valley, California regularly lead to exceedances of the federal air quality standards for PM10 (diameter less than 10 μm). To determine if there are differences in the composition and biological response to Imperial County PM by size, ambient PM samples were collected from a sampling unit stationed in the northern-most part of the valley, South of the Salton Sea. Ultrafine, fine, and coarse PM samples were collected and extracted separately. Chemical composition of each size fraction was obtained after extraction by using several analytical techniques, and biological response was measured by exposing a cell line of macrophages to particles and quantifying subsequent gene expression. Biological measurements demonstrated coarse PM induced an inflammatory response in macrophages measured in increases of inflammatory markers IL-1ß, IL-6, IL-8 and CXCL2 expression, whereas ultrafine and fine PM only demonstrated significant increases in expression of CYP1a1. These differential responses were due not only to particle size, but to the distinct chemical profiles of each size faction as well. Community groups in Imperial Valley have already completed several projects to learn more about local air quality, giving residents access to data that provides real-time levels of PM2.5 and PM10 as well as recommendations on health-based practices dependent on the current AQI (air quality index). However, to date there is no information on the composition or toxicity of ambient PM from the region. The data presented here could provide more definitive information on the toxicity of PM by size, and further inform the community on local air quality.

A rolling forecast approach for next six-hour air quality index track

Current daily AQI forecasting results do not help people making decision to undertake outdoor activities in the coming hours effectively, because the air quality index (AQI) varies greatly at different times of the day. In this study, an approach to providing a rolling forecast of the next six-hour AQI in the seven air quality areas of Taiwan by using big data analysis is proposed. The segmented fitted characteristic curves for the intervals midnight, early morning, morning, afternoon, evening, and night by using the piecewise cubic spline method was potted based on the monitoring data of the past three years to establish the rolling forecast model, which can fully match the local environmental and climatic characteristics. The principle of the proposed rolling forecast track is as follows: (1) replace the constant coefficient with the current AQI in the characteristic curves to form the prediction formula, (2) use the sum of the predicted values plus or minus half of the mean standard deviations to set upper or lower boundaries, respectively, of the forecast range, (3) increase the range of boundaries with the forecast hour, and (4) repeat steps (1)–(3) when entering the next hour. The step (1) is an important step for the rolling forecast, which ensures that the deviation of the predicted value is minimized. The average mean absolute normalized gross error ranges of the rolling forecasts were all lower than 9%. These results demonstrate that the proposed rolling forecast model established using monthly characteristic curves can yield highly accurate forecasts. Therefore, the proposed rolling forecast approach can be used by governments to update people about the AQI over the next 6 h.

Spatio-Temporal Correlation Analysis of Air Quality in China: Evidence from Provincial Capitals Data

In China, public health awareness is growing as people get more concerned about the air quality. Based on the air quality index (AQI) of 31 provincial capital cities (2015–2018) in China, we studied the spatio-temporal correlations of air quality between cities. With spatial, temporal and spatio-temporal analysis, we systematically obtained many interesting results where the traditional analyses may be lacking. Firstly, the air quality of cities has spatial spillover and agglomeration effects and further the spatial correlation becomes higher with time. Secondly, there exists temporal correlation between the current AQI and its past values on multiple time scales, which shows certain periodicity. Thirdly, due to the changing characteristics of time, social activities and other factors affect the air quality positively. However, with the panel data model, the coefficients of spatio-temporal correlation vary for different cities.

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.

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.

Is China's air pollution control policy effective? Evidence from Yangtze River Delta cities

Abstract: China is currently facing the threat of serious air pollution and has adopted a series of governance policies. However, the fact that PM2.5 indicator in some regions often reached far higher than the official threshold since 2015 has reflected that China's current Air Quality Index system for measuring and evaluating air quality is still not perfect. Therefore, this paper extended the current air quality indicator system officially used by China. Using improved Entropy-weighted Factor Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, we calculated the air quality scores of 26 cities in China's most economically developed region - the Yangtze River Delta region based on their daily average concentration numbers of main air pollutants from February 2015 to January 2018. Based on the air quality calculation result, we further studied the effectiveness of air pollution control policies of this region by using the Box-Jenkins Model with fuzzy strategy parameter adjustment. Our findings are: the pollution control policy is more effective to Shanghai, Jiangsu and Zhejiang. The air quality score of Shanghai has improved by 51.07% compared with its level before the policy. Meanwhile, the air quality improvement in Zhejiang province has shown the characteristics of “Campaign-style Governance” right before the G20 Summit, especially that in Hangzhou, the host city of the Summit. On the other hand, maybe due to late start of air pollution control programs, the cities of Anhui province haven't shown obvious improvements in air quality, and some cities have even seen deterioration in air quality scores during our study period.

A New Study on Air Quality Standards: Air Quality Measurement and Evaluation for Jiangsu Province Based on Six Major Air Pollutants

China’s current Air Quality Index (AQI) system only considers one air pollutant which has the highest concentration value. In order to comprehensively evaluate the urban air quality of Jiangsu Province, this paper has studied the air quality of 13 cities in that province from April 2015 to March 2018 based on an expanded AQI system that includes six major air pollutants. After expanding the existing air quality evaluation standards of China, this paper has calculated the air quality evaluation scores of cities in Jiangsu Province based on the six major air pollutants by using the improved Fuzzy Comprehensive Evaluation Model. This paper has further analyzed the effectiveness of air pollution control policies in Jiangsu Province and its different cities during the study period. The findings are as follows: there are distinct differences in air quality for different cities in Jiangsu Province; except for coastal cities such as Nantong, Yancheng and Lianyungang, the southern cities of Jiangsu generally have better air quality than the northern cities. The causes of these differences include not only natural factors such as geographical location and wind direction, but also economic factors and energy structure. In addition, air pollution control policies have achieved significant results in Nantong, Changzhou, Wuxi, Yangzhou, Suzhou, Yancheng, Zhenjiang, Tai’an and Lianyungang. Among them, Nantong has seen the biggest improvement, 20.28%; Changzhou and Wuxi have improved their air quality by more than 10%, while Yangzhou, Suzhou, and Yancheng have improved their air quality by more than 5%. However, the air quality of Nanjing, Huai’an, Xuzhou, and Suqian has worsened by different degrees compared that of the last period within the beginning period, during which Suqian’s air quality has declined by 20.07% and Xuzhou’s by 16.32%.

Air pollution characteristics and health risks in Henan Province, China

Events of severe air pollution occurred frequently in China recently, thus understanding of the air pollution characteristics and its health risks is very important. In this work, we analyzed a two-year dataset (March 2014 – February 2016) including daily concentrations of six criteria pollutants (PM2.5, PM10, CO, SO2, NO2, and O3) from 18 cities in Henan province. Results reveal the serious air pollution status in Henan province, especially the northern part, and Zhengzhou is the city with the worst air quality. Annual average PM2.5 concentrations exceed the second grade of Chinese Ambient Air Quality Standard (75μg/m3) at both 2014 and 2015. PM2.5 is typically the major pollutant, but ozone pollution can be significant during summer. Furthermore, as the commonly used air quality index (AQI) neglects the mutual health effects from multiple pollutants, we introduced the aggregate air quality index (AAQI) and health-risk based air quality index (HAQI) to evaluate the health risks. Results show that based on HAQI, the current AQI system likely significantly underestimate the health risks of air pollution, highlighting that the general public may need stricter health protection measures. The population-weighted two-year average HAQI data further demonstrates that all population in the studied cities in Henan province live with polluted air – 72% of the population is exposed to air that is unhealthy for sensitive people, while 28% of people is exposed to air that can be harmful to healthy people; and the health risks are much greater during winter than during other seasons. Future works should further improve the HAQI algorithm, and validate the links between the clinical/epidemiologic data and the HAQI values.

Characterizing multi-pollutant air pollution in China: Comparison of three air quality indices

Multi-pollutant air pollution (i.e., several pollutants reaching very high concentrations simultaneously) frequently occurs in many regions across China. Air quality index (AQI) is used worldwide to inform the public about levels of air pollution and associated health risks. The current AQI approach used in China is based on the maximum value of individual pollutants, and does not consider the combined health effects of exposure to multiple pollutants. In this study, two novel alternative indices – aggregate air quality index (AAQI) and health-risk based air quality index (HAQI) – were calculated based on data collected in six megacities of China (Beijing, Shanghai, Guangzhou, Shjiazhuang, Xi'an, and Wuhan) during 2013 to 2014. Both AAQI and HAQI take into account the combined health effects of various pollutants, and the HAQI considers the exposure (or concentration)-response relationships of pollutants. AAQI and HAQI were compared to AQI to examine the effectiveness of the current AQI in characterizing multi-pollutant air pollution in China. The AAQI and HAQI values are higher than the AQI on days when two or more pollutants simultaneously exceed the Chinese Ambient Air Quality Standards (CAAQS) 24-hour Grade II standards. The results of the comparison of the classification of risk categories based on the three indices indicate that the current AQI approach underestimates the severity of health risk associated with exposure to multi-pollutant air pollution. For the AQI-based risk category of ‘unhealthy’, 96% and 80% of the days would be ‘very unhealthy’ or ‘hazardous’ if based on AAQI and HAQI, respectively; and for the AQI-based risk category of ‘very unhealthy’, 67% and 75% of the days would be ‘hazardous’ if based on AAQI and HAQI, respectively. The results suggest that the general public, especially sensitive population groups such as children and the elderly, should take more stringent actions than those currently suggested based on the AQI approach during high air pollution events. Sensitivity studies were conducted to examine the assumptions used in the AAQI and HAQI approaches. Results show that AAQI is sensitive to the choice of pollutant irrelevant constant. HAQI is sensitive to the choice of both threshold values and pollutants included in total risk calculation.

The Green Heart Initiative

The Green Heart Initiative

Nanotoxicology: in Vitro–in Vivo Dosimetry

Nanotoxicology: in Vitro–in Vivo Dosimetry

POLUIÇÃO DO AR COMO CAUSA DE MORBIDADE E MORTALIDADE DA POPULAÇÃO URBANA

As reflexões apresentadas no presente texto dizem respeito aos danos causados à saúde pública decorrentes das emissões de contaminantes do ar em áreas urbano-industriais, bem como à premente necessidade de serem revistos os índices adotados como parâmetros de referência de qualidade do ar no Brasil. O texto inicia com um breve histórico dos episódios de poluição do ar mais nocivos a saúde em âmbito mundial, e que atuaram como indicativos da necessidade dos países estabelecerem critérios e parâmetros de controle e monitoramento da qualidade do ar, e desenvolverem instrumentos legais de fiscalização. São abordadas as ações normativas que no Brasil foram implementadas em busca da melhoria da qualidade do ar nas cidades. A comprovada correlação entre os contaminantes do ar e o incremento na incidência de morbidade e mortalidade dos residentes das áreas metropolitanas, e das grandes e médias cidades brasileiras é evidenciada por meio de resultados apresentados na literatura. Tais resultados indicam como necessária a revisão nos limites aplicados para os atuais índices de qualidade do ar, o que sob o ponto de vista mundial, já vem sendo modificado pela OMS. São comentados os programas de controle da poluição do ar que vêm sendo desenvolvidos em âmbito nacional, e ainda os desafios que os órgãos ambientais devem enfrentar para fazerem frente aos agravos à saúde ocasionados pela concentração de contaminantes presentes nas áreas urbano-industriais.