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Air quality indices: A review
Reviews in Environmental Science and Bio/Technology
Abstract
National directives on air quality oblige nations to monitor and report on their air quality, allowing the public to be informed
on the ambient pollution levels. The last is the reason for the always increasing interest, demonstrated by the number of
publications on this topic in recent years, in air quality/pollution indices: since the concentration of individual pollutants
can be confusing, concentration measurements are conveniently transformed in terms of an air quality index. In this way, complex
situations are summarized in a single figure, letting comparisons in time and space be possible. In this paper we will give
an overview about the Air Quality/Pollution Indices proposed in literature and/or adopted by countries, trying also to categorize
them into homogeneous groups. For the classification different approaches can be followed. Since in real life exposure to
mixtures of chemicals occurs, with additive, synergistic or antagonistic effects, here we will distinguish between indices
that consider the conjoint effect of pollutants and indices only based on the actual most dangerous pollutant. This brief
review on air pollution indices shows, on one side, the wide interest in the problem, on the other, the lack of a common strategy
which allows to compare the state of the air for cities that follow different directives. The main differences between the
indices will be also described.
KeywordsAir quality indices–Pollution indices
... In the U.S., the AQI aims to provide an easy-to-understand daily report on air quality in a format that is the same from state to state in the country. The AQI focuses on health effects that an individual might experience within a few hours or days after breathing polluted air (see Plaia and Ruggieri, 2011). ...
... https://ww2.arb.ca.gov/resources/ In other words, scales are set up so that a given AQI for ozone is in the same "place" relative to health effects as the same AQI for CO and for P M (see Environmental Protection Agency, 2018;Plaia and Ruggieri, 2011). Volume defines a ratio scale. ...
... In a series of papers beginning with Ott (1978), authors have studied ways to minimize ambiguity of conclusions from air pollution indices, situations when an index reports air to be highly polluted when it is not, and to minimize eclipsicity of such conclusions, situations when highly polluted air is reported as less so. The former of course raises unnecessary alarms and the latter provides a false sense of security (Plaia and Ruggieri, 2011), potentially rendering conclusions from air pollution measurement useless. Developing indices for level of ambiguity and eclipsicity would provide a way to determine the degree to which indices of air pollution are useful, and would also help in determining ways to minimize ambiguity and/or eclipsicity. ...
Often information relevant to a decision is summarized in an index number. This paper explores conditions under which conclusions using index numbers are relevant to the decision that needs to be made. Specifically it explores the idea that a statement using scales of measurement is meaningful in the sense that its truth or falsity does not depend on an arbitrary choice of parameters; the concept that a conclusion using index numbers is useful for the specific decision that needs to be made; and the notion that such a conclusion is legitimate in the sense that it is collected and used in a way that satisfies cultural, historical, organizational and legal constraints. While meaningfulness is a precisely defined concept, usefulness and legitimacy are not, and the paper explores properties of these concepts that lay the groundwork for making them more precise. Many examples involving two well-known and widely-used index numbers, body mass indices and air pollution indices, are used to explore the properties of and interrelationships among meaningfulness, usefulness, and legitimacy.
... Second, after categorizing the exposure levels to environmental pollutants based on daily cumulative concentrations, we analyzed the data by comparing days with high and low cumulative concentrations. Interestingly, we found that the exposure levels on both high and low concentration days were still within the 'good' or 'moderate' categories as defined by the WHO 34 , the U.S. AQI 35 , and the Korean Ministry of Environment 36 for daily PM exposure (see Supplementary Table 1). Nevertheless, our findings clearly demonstrate that even within these 'acceptable' levels, increased exposure to PM significantly reduces heart rate variability in sensitive and vulnerable populations. ...
... Notably, a negative association with SDNN was observed in both the overall environmental disease and vulnerable groups, with statistically significant associations generally observed at the 90th percentile of exposure. Although the daily average PM levels for both indoor and outdoor environments were within the globally recommended 'good' and 'moderate' categories 34,35,36 , our results suggest that individuals with sensitive characteristics may be more strongly affected by decreased HRV when exposed to a complex mixture of particles of varying sizes. These findings were made possible by the application of BKMR, a sophisticated statistical approach that allows for the modeling of highdimensional and complex exposure-response relationships 29 . ...
Particulate matter (PM) has various health effects, and individuals are simultaneously exposed to these factors. Vulnerable and susceptible individuals are more sensitive to environmental factors than nonvulnerable individuals. Exposure to PM causes cardiovascular diseases. Heart rate variability (HRV) is a biomarker that may be used to identify cardiovascular diseases, and sensitive monitoring of HRV is required. Most previous studies have evaluated exposure using environmental pollution monitoring devices located in various districts. There is a lack of research exploring the relationship between environmental pollutant exposure in personal living spaces and HRV using both indoor and outdoor measurement devices. This study aimed to investigate the association between exposure to PM and HRV using a model capable of multi-substance analysis in short-term exposures, in vulnerable and susceptible individuals, including patients with environmental disease (patients with arrhythmia, chronic airway disease, and stroke patients) and vulnerable populations (residents of an industrial complex area, the elderly). We measured PM 1.0 , PM 2.5 , PM 10 , and digital biomarkers in 97 participants. We evaluated the impact of short-term PM exposure on 24-h HRV over five days by measuring indoor and outdoor exposure using personalized monitoring equipment and ECG monitoring via wearable devices. The PM was calculated as a daily cumulative value and divided into days with high and low cumulative concentrations. The association between exposure to single particulate and complex mixtures and HRV was compared using multiple linear regression and Bayesian kernel machine regression (BKMR). This study found that HRV showed a negative trend with increased PM exposure on days with high cumulative PM concentrations, with statistically significant associations observed between higher PM concentrations and decreased HRV on days with high exposure. The subgroup analysis revealed that patients with chronic airway disease and residents of industrial complex areas exhibited stronger negative correlations between exposure to PM and HRV. These associations were more pronounced with complex exposure to PM 1.0 , PM 2.5 , and PM 10 . In short-term exposure, it was confirmed that exposure to single and complex PM is negatively associated with HRV, and this relationship varies depending on the sensitive characteristics of individuals. Integrating indoor and outdoor personalized exposure assessments with 24-hour ECG monitoring has reinforced our understanding of the complex interactions between PM and health. Our findings indicate that even 'acceptable' PM levels can harm HRV, suggesting that current thresholds may not adequately protect sensitive individuals. This highlights the need for more stringent, particle size-specific standards for at-risk groups.
... While some studies suggest that there is a lack of public awareness about the relation between air pollution and ill health (Bickerstaff and Walker 2001;Semenza et al. 2008), others find empirical evidence that real-time air quality information can be a way to protect the public's health from the threat of air pollution (Wen, Balluz, and Mokdad 2009;Yoo 2021). The air quality index (AQI) is considered as an essential indicator for communicating the air quality status to the public (Plaia and Ruggieri 2011;Shooter and Brimblecombe 2009). ...
... Several obstacles need to be overcome in order to utilize the urbanAQF system as an instrument for public health recommendations. Different AQI definitions are used across cities, with differences in the included pollutants, averaging times, and different threshold values to divide the value ranges on a colour scale (Plaia and Ruggieri 2011). Many studies have shown that population exposures are underestimated in urban areas, when the mobility of a population and the time spent in different urban environments is neglected (Ramacher and Karl 2020; Singh, Sokhi, and Kukkonen 2020; Soares et al. 2014). ...
This article describes the forecasting system urbanAQF, which incorporates several developments to deal with the complexities of air pollution in cities, including the adaptation of high-resolution numerical weather prediction data to the urban canopy, the coupling with regional forecast data, and an interactive web service for public dissemination of urban air quality information. The system applies a unique bias correction algorithm that adjusts boundary conditions and traffic emissions to observations of the previous days. An evaluation of the air quality forecasts during 2021 for Hamburg, Germany, against a comprehensive dataset of the administrative monitoring network and meteorological data, demonstrated the system’s capability to describe space and time variations of NO2 and PM10. At traffic sites, the high number of missed alerts in relation to exceedance of the daily mean limit for NO2 indicates the need to improve the simulation of traffic emissions. The forecast of PM2.5 alerts was affected by the time lag of the automatic correction, leading to a low number of correct alerts. The overall performance for O3 was very good, despite frequent false alarms connected to the prediction of unstable atmospheric conditions. The urbanAQF system empowers policymakers to implement effective measures for improving air quality in cities.
... By superimposing the risk information on the trends shown in graphs, moments of worse indoor air quality are easy to identify, even by non-experts (Leyva Pernia 2019; Schalm et al. 2019). For human health, several kinds of air quality indices (AQIs) exist to warn the public when outdoor air pollution is dangerously high (Tan et al. 2021;Plaia and Ruggieri 2011;Kanchan et al. 2015;Zhu and Li 2017;Leyva Pernia 2019). The AQIs could be merged with the environmental measurements and enhance the information in graphs. ...
... This polynomial is not used as a conversion function because there is no knowledge available about the trend between the points as is suggested by the polynomial and because we wanted to use a similar way of working as the indoor air quality assessment for heritage objects. In addition, the concept of a piecewise linear function is also used in other air quality indices (Zhu and Li 2017;Plaia and Ruggieri 2011). The conversion function is defined by the 5 reference points defined in Table 1. ...
The environmental conditions in a conservation-restoration studio for paintings induce an inherent risk to objects of art and to humans working on those objects. They are both subject to (sometimes dangerous) chemical substances and fluctuations in environmental conditions (e.g., temperature, relative humidity). In this paper, we report on a measuring campaign which lasted more than a year collecting data about the air quality within a painting studio of a higher education institute. An existing algorithm assessed the indoor air quality for heritage objects using international air quality standards. This contribution presents a new algorithm to assess indoor air quality for human health relying on thresholds imposed by legislation and recommended by reference institutes. This algorithm has been applied to the same measuring campaign. The assessments illustrate that the same environmental conditions have a different impact on canvas paintings, panel paintings, students, and staff. Air quality is thus a relative concept that depends on the object/subject that is considered in the analysis.
Graphical abstract
... The air quality index (AQI) is intended for public use so that individuals are aware of air quality and potential health effects. The AQI uses a simple formula and the data is presented using the well-known 'traffic light' scale, making it straightforward for the public to comprehend (13). However, the AQI reflects air quality only as the amount of a pollutant with the highest subindex without considering the possible combined effects of simultaneous exposure to multiple pollutants and ignoring variations in the characteristics of relationships between health outcomes and air pollutants in different countries or regions (13). ...
... The AQI uses a simple formula and the data is presented using the well-known 'traffic light' scale, making it straightforward for the public to comprehend (13). However, the AQI reflects air quality only as the amount of a pollutant with the highest subindex without considering the possible combined effects of simultaneous exposure to multiple pollutants and ignoring variations in the characteristics of relationships between health outcomes and air pollutants in different countries or regions (13). Therefore, the index does not reflect the non-threshold concentration response relationship between air pollutants and health risks (14)(15)(16). ...
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.
... Worldwide urban air quality is often assessed using national limit values for individual air pollutants relevant to human health [1][2][3][4][5][6]. To account for the mix of air pollutants that people breathe, air quality indices (AQIs) have been developed to supplement stand-alone assessments of individual air pollutants, particularly for applications in urban planning and for providing information to the population on the internet about overall air quality [7][8][9][10][11]. The air pollutants recorded over a long term at ground-based air quality monitoring stations (AQMSs) are commonly used for determining AQIs. ...
... Historically, air pollution assessments according to individual air pollutants were based only on whether the national limit values were exceeded. However, graded assessments, for which there is a need for various reasons [7][8][9][10][11], are impossible with this method. ...
Seoul has a high density of air quality monitoring stations (AQMSs) grouped into roadside, urban, and background types. Using the extensive data from 42 AQMSs in the period 2018 to 2021, the statistical characteristics of air pollutants required to calculate the daily air quality index DAQx* (daily maximum 1 h O3 and NO2 means and daily 24 h PM10 and PM2.5 means) are determined, depending on station types and three temporal periods (individual years, winters, and summers). The results for (i) annual cycles, which include peak concentrations of PM10 (up to 517 µg/m3 in May 2021) and PM2.5 (up to 153 µg/m3 in March 2019) owing to transboundary transport, (ii) annual medians, (iii) annual scattering ranges, (iv) partitioning of frequencies into DAQx*-related concentration ranges, and (v) maximum daily variations within individual station types indicate clear statistical air pollutant characteristics depending on the station types. They were primarily caused by different emission and atmospheric exchange conditions in a circular buffer around each AQMS, which are often approximated by urban form variables. The maximum daily variations were highest in the middle NO2 concentration range of the “satisfying” class for the roadside type (between 53% in summer 2019 and 90% in winter 2020).
... The air quality index (AQI) is determined by the highest value of the individual air quality index (IAQI) corresponding to each pollutant. The classification of each pollutant is based on the air quality index reference guide (29). ...
Background
Aerosols can affect human health through mechanisms like inflammation, oxidative stress, immune dysregulation, and respiratory impairment. In high-pollution areas, airborne particles may promote the transmission of pathogens such as Mycobacterium tuberculosis. This study investigates the spatiotemporal distribution of tuberculosis, its association with air pollution, and potential sources in the geographically unique Kashgar region of Xinjiang, encircled by mountains and desert.
Methods
Kriging interpolation and time series observation were used to analyze spatiotemporal trends and identify hot and cold spots of tuberculosis (TB) incidence and air quality in Xinjiang from 2011 to 2023. Kruskal-Wallis and multiple comparisons were applied to assess regional differences. Meteorological clustering and trajectory analysis identified pollutant pathways and potential source areas, with hypotheses proposed for TB transmission routes.
Results
The interaction between tuberculosis, the geographic environment, and aerosols in Xinjiang reveals a consistent spatial distribution of air quality index (AQI) and TB incidence, with overlapping hotspots and cold spots. The incidence rate of tuberculosis is “n/100,000.”Southern Xinjiang, shows higher TB incidence (235.31 ± 92.44) and poorer air quality (AQI: 64.19 ± 11.73) compared to Northern Xinjiang (TB: 83.82 ± 21.43, AQI: 53.90 ± 6.48). Significant regional differences in TB incidence (p < 0.0001) were confirmed, with post-hoc analyses indicating higher TB rates and worse air quality in Southern Xinjiang. Trajectory and concentration-weighted trajectory (WCWT) analysis identified dust from the Taklimakan Desert as a major contributor to PM2.5 and PM10 pollution, with values exceeding 150 μg/m³ for PM2.5 and 400 μg/m³ for PM10 in key areas like Aksu and Kashgar. The Kunlun and Tianshan mountain ranges serve as barriers that trap migrating dust, while meteorological patterns indicate that dust-laden trajectories extend further into the mountainous areas. This phenomenon exacerbates the spread of tuberculosis (TB) in the high-risk regions of southern Xinjiang.
Conclusion
The study highlights a distinct interaction between TB, the geographic environment, and aerosols in southern Xinjiang. Poor air quality and elevated TB incidence overlap, particularly in Kashgar. Here, dust from the Taklimakan Desert, trapped by the Kunlun and Tianshan mountains, intensifies PM2.5 and PM10 pollution, further contributing to TB transmission in high-risk areas.
... To preserve human health and environmental resources, AQI used to explain the degree of urban air contamination. It's also used to evaluate pollution-reduction efforts and track changes in surrounding air excellence (Plaia, 2011). The AQI is a daily collection of ambient air pollutants monitoring that is reported using an index or rating scale (Van den Elshout et al., 2008). ...
With the rate of fast urbanization, the devasting effects of air pollution are spreading across the globe. Due to its connection to people's health, air quality should be given more importance than other environmental measures. Air pollution is considered a cause of many human diseases. Therefore, this study intends to investigate seasonal variation of air quality and "Air Quality Index (AQI)" in Chittagong city due to its volume, large population density, and importance as a commercial capital city of Bangladesh. Air pollution data on PM 10 , PM 2.5 , NO 2 , NOx, SOx, CO, and O 3 levels have been collected from TV station, Khulshi Continuous Air Monitoring Station (CAMS). Component-specific analyzers have been used to continuously measure trace gases where O 3 is observed with a UV photometric analyzer. This study detect the highest peak (PM 2.5 = 93.5 µg/m 3 , PM 10 = 210 µg/m 3) in January and the lowest concentrations (PM 2.5 = 14.6 µg/m 3 and PM 10 = 26.9 µg/m 3) during July and August. The highest average concentration has been recorded as the value of SO 2 = 12.8 ppb (monsoon season), NO 2 = 64.9 ppb (pre-monsoon), CO= 1.2 ppm (monsoon) and lowest SO 2 = 3.2 ppb (winter season), NO 2 = 24.4 ppb (monsoon), CO= 0.6 ppm (pre-monsoon) respectively. The AQI values (223.6), (109.5), (194.5), and (317.3) indicate that the air quality during the pre-monsoon, monsoon, post-monsoon, and winter season is very unhealthy, cautious, unhealthy, and extremely unhealthy, respectively.
... Notably, a negative association with SDNN was observed in both the overall chronic disease and control groups, with statistically significant associations generally observed at the 90th percentile of exposure. Although the daily average PM levels for both indoor and outdoor environments were within the globally recommended 'good' and 'moderate' categories [31][32][33] , our results suggest that individuals with sensitive characteristics may be more strongly affected by decreased HRV when exposed to a complex mixture of particles of varying sizes. These findings were made possible by the application of BKMR, a sophisticated statistical approach that allows for the modeling of high-dimensional and complex exposure-response relationships 28 . ...
Particulate matter (PM) exposure can reduce heart rate variability (HRV), a cardiovascular health marker. This study examines PM1.0 (aerodynamic diameters <1 μm), PM2.5 (≥1 μm and <2.5 μm), and PM10 (≥2.5 μm and <10 μm) effects on HRV in patients with environmental diseases as chronic disease groups and vulnerable populations as control groups. PM levels were measured indoors and outdoors for five days in 97 participants, with 24-h HRV monitoring via wearable devices. PM exposure was assessed by categorizing daily cumulative PM concentrations into higher and lower exposure days, while daily average PM concentrations were used for analysis. Results showed significant negative associations between exposure to single and mixtures of different PM metrics and HRV across all groups, particularly in chronic airway disease and higher air pollution exposed groups. These findings highlight that even lower PM levels may reduce HRV, suggesting a need for stricter standards to protect sensitive individuals.
... However, only criteria air pollution or air quality were predicted by the aforementioned studies, but none of the studies have predicted the AQI and AQHI. That tends to minimize the impact of air pollution effect on human health, especially elders and children (23,24). In the present study, the time series methods were used to determine and predict AQI and AQHI in Ahvaz city, south-west of Iran. ...
Background: Air pollution indexes are used to indicate the level, quantity, and quality of air pollution. The air quality index (AQI) and air quality health index (AQHI) have been developed to report their association with human health. Methods: Air quality variables (PM2.5, O3 , and NOx ) per hour were obtained for March 2021 to March 2022 from three central pollution control centers in Ahvaz, the capital city of Khuzestan province, southwest of Iran. R3-3-4, Minitab-17, and SPSS-19 software were used to analyze the obtained data. Results: In this study, AQHI and AQI were predicted with actual data, forecasting the air quality with two confidence interval percentages of 95 and 80 illustrated for future days. Also, the relationship between AQI and AQHI was determined; hence, this relationship is important for some cities for which the AQHI index is not measurable. AQHI can be determined for each place using AQI values and the obtained relationship. Conclusion: The result indicated that we could also forecast the AQI and AQHI for future days and obtain a new equation for AQHI due to AQI values.
... The health effects of atmospheric pollution have raised widespread social concern. The air quality index (AQI) is commonly used internationally for air quality evaluation, describing the cleanliness or pollution of the air is and its impact on health (Plaia and Ruggieri 2011). However, the AQI only reflects air quality conditions based on the pollutant with the highest sub-index, degrading the combined effects of other co-occurring pollutants. ...
Understanding the risk trade-offs between nitrogen dioxide (NO2) and ozone (O3) pollution is crucial for ozone governance. The air quality health index (AQHI) provides a more comprehensive measure of air pollution mixtures. This study used environmental, meteorological, and health data of 13 cities in the Beijing–Tianjin–Hebei region for 2018–2020 to assess the health effects of pollutants during both cold and warm seasons. The study reveals that NO2 pollution in the cold season (20.4–63.4 µg/m³) is more severe compared to warm season (18.3–49.7 µg/m³), and its concentrations have been decreasing annually in most cities. However, the study also highlights a concerning trend of increasing ozone concentrations during the cold season across all cities in the region (The average annual increase is 3.5 µg/m³). This increase may be linked to the abatement of nitrogen oxides (NOX) and particulate matter (PM). The health benefit of reducing environmental air NO2 concentrations maybe offset by the increase in O3 concentrations. Emission control measures to reduce nitrogen dioxide, sulfur dioxide, and particulate matter levels have been effective in reducing the negative impacts on health caused by air pollution in various cities in the Beijing–Tianjin–Hebei region. It was necessary to construct the cold season AQHI (AQHI-C) and warm season AQHI (AQHI-W) separately in the Beijing–Tianjin–Hebei region, especially for the assessment of health risks during the cold season.
... Then, in an attempt to correct the inadequacies of the first model, a second model is developed. This procedure is repeated until the optimum amount of models have been introduced, or until the entire training dataset has been successfully anticipated, whatever comes sooner [17]. ...
Air quality prediction is a critical component in environmental monitoring and public health management. Traditional forecasting methods often fall short in capturing the complex, non-linear interactions among various atmospheric factors. This paper introduces a novel approach to air quality prediction that leverages a combination of advanced machine learning techniques, including Long Short-Term Memory (LSTM) networks, and other deep learning models alongside robust optimization strategies. By integrating statistical models, deep learning, and machine learning algorithms, we aim to significantly enhance the accuracy and reliability of air quality forecasts. Our methodology employs an ensemble of models, such as LSTM, convolutional neural networks (CNNs), and gradient boosting machines (GBMs), to capture both temporal and spatial dependencies in air quality metrics. To further refine predictions, we incorporate various optimization techniques that improve the performance and efficiency of the learning process. Comparative analysis with traditional methods demonstrates the superiority of our approach in terms of prediction accuracy and computational efficiency. The results indicate a marked improvement in forecasting capabilities, which can be instrumental in devising timely interventions and mitigating adverse health effects associated with poor air quality. This research not only advances the state-of-the-art in air quality prediction but also provides a scalable framework applicable to other environmental monitoring applications.
... It educates the public and policymakers on the gravity of air pollution and the detrimental impact it can have on human health in an effort to protect the environment and human health. In addition, it is employed to assess pollution-reduction efforts and monitor changes in ambient air quality [91]. ...
The multilayer perceptron (MLP) neural network is a widely adopted feedforward neural network (FNN) utilized for classification and prediction tasks. The effectiveness of MLP greatly hinges on the judicious selection of its weights and biases. Traditionally, gradient-based techniques have been employed to tune these parameters during the learning process. However, such methods are prone to slow convergence and getting trapped in local optima. Predicting urban air quality is of utmost importance to mitigate air pollution in cities and enhance the well-being of residents. The air quality index (AQI) serves as a quantitative tool for assessing the air quality. To address the issue of slow convergence and limited search space exploration, we incorporate an opposite-learning method into the Jaya optimization algorithm called EOL-Jaya-MLP. This innovation allows for more effective exploration of the search space. Our experimentation is conducted using a comprehensive 3-year dataset collected from five air quality monitoring stations. Furthermore, we introduce an external archive strategy, termed EOL-Archive-Jaya, which guides the evolution of the algorithm toward more promising search regions. This strategy saves the best solutions obtained during the optimization process for later use, enhancing the algorithm’s performance. To evaluate the efficacy of the proposed EOL-Jaya-MLP and EOL-Archive-Jaya, we compare them against the original Jaya algorithm and six other popular machine learning techniques. Impressively, the EOL-Jaya-MLP consistently outperforms all other methods in accurately predicting AQI levels. The MLP model’s adaptability to dynamic urban air quality patterns is achieved by selecting appropriate values for weights and biases. This leads to efficacy of our proposed approaches in achieving superior prediction accuracy, robustness, and adaptability to dynamic environmental conditions. In conclusion, our study shows the superiority of the EOL-Jaya-MLP over traditional methods and other machine learning techniques in predicting AQI levels, offering a robust solution for urban air quality prediction. The incorporation of the EOL-Archive-Jaya strategy further enhances the algorithm’s effectiveness, ensuring a more efficient exploration of the search space.
... In the first experiment, the focus was on testing the accuracy of the machine learning model in predicting air quality indices (AQI) [8]. The setup involved comparing the model's predictions against actual AQI measurements using a split dataset approach. ...
This paper addresses the critical environmental challenge of air quality degradation, exacerbated by industrial emissions, vehicular pollutants, and agricultural activities [1]. Our proposed solution, a Real-Time and Fine-Granularity Air Quality Monitoring and Analytical System, leverages machine learning and drone technology to dynamically monitor and analyze air quality across diverse locations and altitudes. By integrating drone-mounted sensors, advanced machine learning algorithms, and a user-friendly interface, the system offers unprecedented spatial and temporal resolution in air quality assessment. The study navigated through limitations such as data transmission reliability and the complexity of real-time data analysis, employing robust communication protocols and enhanced analytical models for improved accuracy [2]. Experimentation across various urban and rural settings demonstrated the system's effectiveness in identifying pollution hotspots and predicting air quality trends, with significant improvements over traditional stationary monitoring methods. Our findings highlight the potential of combining drone mobility with machine learning efficiency to revolutionize air quality monitoring, making it an indispensable tool for environmental management and public health protection [3].
... The air quality index (AQI) has come to be used over the years to express the level of pollutant concentration over a period in a way that is understandable by the public and decision-makers [1]. Researchers have alluded to the lack of a systematic process for hybrid ML models and the possibility of generating an unlimited number of scenarios by combining different models to maximize potential; this could scale up AQP research while potentially precluding practical application [2]. ...
This paper discusses air quality index (AQI) representation using a fuzzy logic framework to cover the blurry areas of AQI where indices are in between ranges of values. After studying several standards for air quality prediction (AQP), this research suggested the use of fuzzy logic as an extended method to cover some limitations found in several standards, in which the fuzzy logic represents a more dynamic way to support cross-country comparisons as well. This research expanded upon the United States Environmental Protection Agency (USEPA) standards to address their acknowledged limitations by constructing a fuzzy air quality levels prediction (FAQLP) model, which categorizes air quality into corresponding ranges (actual levels) and classifies new fuzzy levels (predicted levels), using a fuzzy logic model (to enforce more realistic predictions). This model can solve the issue of values at or near boundaries when there is uncertainty about air quality levels. The study aims to incorporate a comparative study of two urban settings providing dynamic machine-learning modeling approaches for advanced air pollution control. The DNN–Markov model is presented in this paper as the selected hybrid model for AQI prediction, and the adaptive neuro-fuzzy inference system (ANFIS) was used to represent AQI. This work presents a novel air quality index framework that consists of a DNN–Markov model for accurate hourly predictions and air quality level representations using ANFIS.
... The classification of each pollutant is based on the air quality index reference guide. [28] . ...
Objective
Air pollution can impact the human immune system through inflammatory responses, oxidative stress, immune regulation, respiratory health, and various other mechanisms. Particulate matter (PM10, PM2.5) in the aimay act as a carrier, facilitating the spread of tuberculosis bacilli among populations.However, the complex nature of real-world environments often makes experimentalsimulation challenging.This study starts from the spatial dimension, simulates the transport of pollutants and analyzes the incidence of tuberculosis and the distribution characteristics of pollutants in the typical area of surrounded by three mountains and facing the desert: Kashgar region.
Methods
The distribution characteristics of pollutants and tuberculosis epidemic areas were analyzed by Kriging interpolation
... The Air Quality Index (AQI), which divides the level of air pollution into a numerical rating system with the more significant the value, the greater the air pollution, according to Plaia and Ruggieri [11], presents insight into real-time ambient air quality about the measures of PM2.5 associated health issues. To raise the general public's consciousness and encourage people to take action to preserve their physical health, the AQI relies on the health risks that people may experience due to exposure to PM2.5 [12]. ...
The significant air pollution issue in Nigeria has sparked severe widespread fear. This research is a part of the effort to eliminate or reduce this threat. The research aimed to examine the distributions of Air quality index (AQI), Particulate matter with less than 2.5 µm diameter (PM2.5), Relative humidity (RH), Wind speed, and Temperature (temp) in nineteen (19) Nigerian states, including the Federal Capital Territory (FCT). To accomplish this, satellite data obtained from IQAir's air quality monitoring platform for nineteen states and the Federal Capital Territory of Abuja between the 7th and 17th of October 2021 were used and statistically analyzed. The inverse distance weighting (IDW) interpolation method was applied to observe the spatial distribution of the data. The average AQI was 72.11, with minimum and maximum values of 26 and 17, respectively. The mean of PM2.5 was 28.71±13.7 µg/m 3. The following meteorological average values were recorded: RH (62.43%), wind speed (6.96 m/s), and temperature (28.71 o C). The AQI was between good and unhealthy, the PM2.5 was more than 17% (annual) and 52% (24 h), which is higher than World Health Organisation (WHO) limits, and the correlations between AQI, PM2.5 and meteorological parameters were weak. According to the data, PM2.5 was not distributed uniformly across the regions but varied spatially and temporally. It is advised that people check their local daily air pollution forecasts, avoid exercising outside, avoid working out in crowded areas, and use less energy at home to prevent an increase in AQI. It is recommended to monitor and maintain the air quality and minimize harmful anthropogenic activities to mitigate the threat.
... In fact, most watchOS apps are accompanied by corresponding iOS apps due to technical requirements and user preference [25]. We used the United States Environmental Protection Agency's Air Quality Index to categorize PM2.5 concentrations [46]. We used consensus proposed by Lowther et al. [35] to categorize CO 2 concentrations as a proxy for indoor ventilation, human bio-effluent, and other indoor air pollutants: ≤1,000PPM: good, 1000-1500PPM: moderate, and ≥1500PPM: poor. ...
The last decade witnessed the popularization of commercial smartwatches as personal health-tracking devices. This coincided with a deeper understanding of the health effects of ambient and indoor air pollution. Consequently, there is a growing interest in interfacing data from portable sensors with commercial smartwatches, allowing users to monitor the air whenever and wherever. In the real world, it remains insufficiently investigated how and through which modalities users benefit from smartwatch interfaces. To bridge this gap, we conducted a pioneering in-the-wild usability study with myAQM (N = 9). myAQM visualizes PM2.5 and CO2 data from a portable air quality monitor on different Apple Watch interfaces: the main application, complications (watch face’s widgets), and notifications. Our qualitative analysis showed that the Apple Watch interfaces provided glanceable data, omnipresent reminders of the surrounding air, and timely alerts of changes in air quality. Through these affordances, users developed a more acute understanding of the surrounding air and reduced self-exposure to bad air quality through certain behavioral changes, if circumstances permitted. Our findings support commercial smartwatches as a convenient and readily-accessible add-on for traditional smartphone interfaces in disseminating hyperlocal air quality data.
... The air quality index (AQI), which is defined based on the concentration of five main air pollutants (CO, SO2, PM10, O3, and NO2), is used to inform the public about the severity of air pollution [4]. AQI is a simple and understandable method to measure the impact of air quality on human health [5]. The higher the AQI value, the greater the level of air pollution and the greater health concern [6]. ...
Industrial development has made air pollution increasingly severe, and many respiratory diseases are closely related to air quality in terms of infection and transmission. In this work, we used the classic stochastic susceptible–infectious–recovered (SIR) model to reflect the spread of respiratory disease, coupled with the diffusion process of air pollutants to the infectious disease model, and we investigated the impact of various environmental noises on the process of disease transmission and air pollutant diffusion. The value of this study lies in two aspects. Mathematically, we define threshold R1s for extinction and threshold R2s for persistence of the disease in the stochastic model (R2s<R1s) when the parameters are constant, and we show that (i) when R1s is less than 1, the disease will go to stochastic extinction; (ii) when R2s is larger than 1, the disease will persist almost surely and the model has a unique ergodic stationary distribution; (iii) when R1s is larger than 1 and R2s is less than 1, the extinction of the disease has randomness, which is demonstrated through numerical experiments. In addition, we derive the exact expression of the probability density function of the stationary distribution by solving the corresponding Fokker–Planck equation under the condition of disease persistence and analyze the effects of random noises on stationary distribution characteristics and the disease extinction. Epidemiologically, the change of the concentration of air pollutants affects the conditions for disease extinction and persistence. The increase in the inflow of pollutants and the increase in the clearance rate have negative and positive impacts on the spread of diseases, respectively. We found that an increase in random noise intensity will increase the variance, reduce the kurtosis of distribution, which is not conducive to predicting and controlling the development status of the disease; however, large random noise intensity can also increase the probability of disease extinction and accelerates disease extinction. We further investigate the dynamic of the stochastic model, assuming that the inflow rate switches between two levels by numerical experiments. The results show that the random noise has a significant impact on disease extinction. The data fitting of the switching model shows that the model can effectively depict the relationship and changes in trends between air pollution and diseases.
... Secondly, the air quality index (AQI) is a nonlinear, dimensionless assessment of air quality conditions affecting a city's health and environment (Plaia and Ruggieri, 2011). Additionally, the AQI is available to the public to determine the air pollution level in a given city (Chaudhuri andChowdhury, 2018, Li et al. 2015). ...
This paper aims to explore the relationship between the Air Quality Index (AQI), COVID-19 incidence rates, and population density within Malaysia’s ten most populous cities from January 2018 to December 2021. Data were sourced from the Department of Statistics Malaysia, the World Air Quality Index Project, and Our World in Statistics. The methodology integrated population-based city classification and AQI assessment, cluster analysis through SPSS, and Generalized Additive Mixed Model (GAMM) analysis using R Studio despite encountering a data gap in AQI for five months in 2019. Cities were organized into three clusters based on their AQI: Cluster One included Ipoh, Penang, Kuala Lumpur, and Melaka, Cluster Two comprised Kuantan, Seremban, Johor Bahru, and Kota Bharu, Cluster Three featured Kota Kinabalu and Kuching. GAMM analysis revealed prediction accuracies for AQI variations of 58%, 60%, and 41% for the respective clusters, indicating a notable impact of population density on air quality. AQI variations remained unaffected by COVID-19, with a forecasted improvement in air quality across all clusters. The paper presents novel insights into the negligible impact of COVID-19 on AQI variations and underscores the predictive power of population dynamics on urban air quality, offering valuable perspectives for environmental and urban planning.
... The air quality index, or sometimes called air pollution index, is a unified index that summarizes the concentrations of various pollutants in a single index. The pollutants are then categorized to determine the efficiency of dissemination discharge and utilization in different regions (Plaia and Ruggieri, 2010). This helps in the reliable comparison of air quality patterns over time and space, as well as the prediction and possible caveat of air pollution (Mirabelli et al., 2020). ...
Emissions emanating from commercial facilities in high dense area are subject of concern as more people are being hospitalised or suffering from long term exposure to poor air quality. Thus, there is the need to conduct assessment of air quality and other hazardous emissions in some commercial facilities. This paper presents the air quality index, formaldehyde and environmental emissions emanating from four service stations in Lagos metropolis. Four different filling stations were strategically selected from dense localities and monitored for about 8hrs per day for 30 days. The level of air qualities is assessed respectively for these locations. The data is time-averaged over the period of the data acquisition, and the results are presented. The air quality index for all the service stations were found to be below the recommended threshold by the World Health Organization (WHO) guidelines. The CO, CO2 and HCHO emissions have similar pattern with respect to all locations monitored. Further analyses of emissions from different stations revealed that there is significant difference in service station B (SS B) from service station D (SS D) for CO2 emissions at 95% confidence as Pvalue (0.04) is less than 0.05. A proportion of 51% of CO2 can be accounted for by the emission variability in the service stations. However, there is no significant difference in the other emissions patterns at 95% confidence as Pvalue is greater than 0.05 (Pvalue >0.05) for AQI, CO, HCHO and TVOC emissions. In general, the emissions within the selected built-up areas were found not to sufficiently harm service station workers for short term exposure.
... This can help them understand how the environment affects air quality, climate, and health (Dominski, et al., 2021;Yang, et al., 2019). Alternatively, one can use Air Quality Index (Plaia, A., & Ruggieri, 2011), which has a different interface. The problem, which can be referred to other apps as well, is that it could be a privacy-risky app as it collects a range of information such as location, IP and stored data. ...
Covid-19 has also had a significant impact on schools, the use of distance learning has raised questions already present, in particular with respect to the meaning of tools and technologies. Leaving aside the aspects related to the use of the network and those of communication, the authors want to provide a brief overview of the fundamental issues related to the use of a smartphone for STEM teaching. A theme that sees two opposing positions (pros and cons) colliding, often unavailable for discussion and dialogue. Without taking a position, the text tackles the problem from three points of view: the hardware, the apps, and some possible activities that can be associated with the main functions activated by the students.
... Therefore, the public and governments have noticed air pollution threatening the environment and human health. Many people die yearly from this problem, including seven million premature deaths (Plaia and Ruggieri, 2011;World Health Organization, 2020a). Furthermore, Vohra et al., (2021) estimated premature deaths worldwide as 10.2 million people yearly. ...
Air pollution, one of humanity's essential environmental problems due to the increasing population and urbanization, negatively affects the ecosystem and public health. During reduced human activity, such as martial law, war, and pandemics like COVID-19, improvements in air quality may be observed due to diminished anthropogenic impact. The novel coronavirus, COVID-19, has caused widespread illness and fatalities. The World Health Organization (WHO) declared a state of emergency at the end of December 2019 following the first recognition of the virus in Wuhan. The Turkish government declared this state on March 11, 2020, and implemented some measures, including a lockdown (LD) and a partial lockdown (PLD), to protect public and human health. The present study aims to determine the impact of LD and PLD on the air quality of fourteen selected cities in Turkey that participated in all LDs during the state of emergency on weekends and national and religious holidays. The hourly air quality data used in the study were collected from 105 air monitoring stations in fourteen cities. The non-parametric Kruskal Wallis test, followed by the Dunn's Bonferroni test for pairwise comparison, was employed to determine the differences in air quality between years. The findings indicated significant reductions in air pollution during LD and PLD: 21.1-40.3% and 8.9-29.8% in PM10, respectively, and 30.2-50.8% and 2.6-22.4% in NOx, respectively. SO2 and CO also varied significantly. While the changes in SO2 during LD and PLD went from 0.0% to 5.7% and-2.4% to 1.2%, respectively, those in CO ranged from-6.6% to 29.6% and 1.3% to 33.2%, respectively.
... The RAQI represents the air quality in a particular area in the past, typically prior to the implementation of air pollution reduction efforts. The Air-GEP index provides a quantitative measure of the improvement in air quality by comparing the CAQI to the RAQI and normalising the difference with the RAQI value (Jarauta-Bragulat et al., 2016;Nigam et al., 2015;Plaia and Ruggieri, 2011). ...
... The choice to use a single pollutant to define the AQI value has certainly been amply criticized over the years, because such simplification may delete important information and correlations present in the holistic data [39][40][41]. Thus, many indices have been proposed that simultaneously take multiple pollutants into account [42][43][44]. The Multisite-Multipollutant Air Quality Index advocated by Plaia and Ruggieri [43] for example, factors both the combination of pollutants and their spatial concentration distribution in an area. ...
... Finalmente, se analiza el índice de la calidad del aire (AQI o ICA) según los niveles de concentración de PM en la ciudad de estudio. El valor del índice de la calidad del aire establece seis categorías de peligrosidad, de modo que cuanto mayor sea el índice, peor será la calidad del aire [19], [20]. A nivel cualitativo, el rango del ICA está dividido en seis tramos: ...
... The determination of the level of concentration of selected atmospheric pollutants and facilitation of reporting on the level of atmosphere pollution employ various air quality indices, frequently proposed by the national services dealing with air quality monitoring, or various research teams (Karavas et al. 2020). Many different air quality indices have been developed over the years, differing in input data, target groups and application (Plaia, Ruggieri 2011, Mandal, Gorai 2014, van den Elshout et al. 2014, Kumar 2022. This paper employs the Common Air Quality Index (CAQI), which was developed as part of the scope of project INTERREG IIIC and IVC, co-financed from the resources of the European Union (van den Elshout et al. 2008). ...
The paper analyses biometeorological conditions in Lublin based on the Universal Thermal Climate Index (UTCI), and air quality based on the Common Air Quality Index (CAQI). The used data were obtained from the database of IMGW-PIB and RDEM, and cover the period 2015–2021. The most frequently occurring biometeorological conditions were classified as no thermal stress. They were observed with a frequency of 34.3%. Conditions unfavourable for the human organism accounted for 65.7% in total, including those belonging to thermal stress classes related to cold stress (52.3%), and heat stress (13.4%). In the analysed years, 75.5% of cases were with very low and low air pollution. High and very high air pollution usually occurred during biometeorological conditions related to cold stress (from slight cold stress to strong cold stress). During extreme thermal phenomena, such as a cold wave (January 2007) and hot wave (August 2015), unfavourable biometeorological conditions were accompanied by low aerosanitary conditions (low air quality). In the analysed period, and particularly in recent years, an improvement in air quality has been observed, potentially associated with limited mobility of people during the COVID-19 pandemic.
... Where, Ip = the index for pollutant p Cp = is the monitored concentration of pollutant p BPHigh = the breakpoint that is greater than or equal to Cp BPLow = the breakpoint that is less than or equal to Cp IHigh = the AQI value corresponding to BPHigh ILow = the AQI value corresponding to BPLow AQI values used in this work follow Environmental Protection Agency (EPA) standards. This is an international agency that describes guidelines and standards in relation to the air pollution concentration levels and its effect as elaborated in Figure 2 [18]. AQI values range from 0 to 500 and is categorized into five categories: good, 0-50; moderate, 51-100; unhealthy for sensitive groups, 101-150; unhealthy, 151-200; very unhealthy, 201-300; and hazardous, 301-500. ...
The environment and human health are both impacted by air quality. In Africa, where air quality monitoring systems are rare or nonexistent, poor air quality has caused far more deaths and environmental damage than anywhere else in the world. Air pollution in Africa is a result of the continent’s growing urbanization, industrialization, road traffic, and air travel. Particularly in Africa, air pollution continues to be a silent killer, and if it is not addressed, it will continue to cause fatal health disorders like heart disease, stroke, and chronic respiratory organ disease. In this study, the potential of IoT is greatly exploited to measure air pollution levels in real time. An Arduino Uno microcontroller board based on the ATmega328P integrated with the Arduino Integrated Development Environment is used to build the prototype. The designed prototype consists of the different sensors that capture air pollutant concentration levels from the environment. All the data pertaining to air quality are monitored in real-time using Thingspeak, an IoT-based platform. The monitoring results are visible through the mobile application developed; as a result, this creates awareness to the public and the concerned policy makers can make well informed decisions.KeywordsAir PollutionInternet of ThingsAir Quality MonitoringThingSpeakLow- and middle-income countries
... The indicators are compared individually, combined into indices, or compared using multivariate statistics, e.g., [70]. Additionally, although comparisons to guidelines or calculations of air quality indices are common practices for estimating potential health effects [71], other objectives, such as estimating downwind influence and understanding patterns in data, are also common [72][73][74]. The example analyses here were focused on the latter. ...
Industrial control charts are used in manufacturing to quickly and robustly indicate the status of production and to prompt any necessary corrective actions. The library of tools available for these tasks has grown over time and many have been used in other disciplines with similar objectives, including environmental monitoring. While the utility of control charts in environmental monitoring has been recognized, and the tools have already been used in many individual studies, they may be underutilized in some types of programs. For example, control charts may be especially useful for reporting and evaluating data from regional surveillance monitoring programs, but they are not yet routinely used. The purpose of this study was to promote the use of control charts in regional environmental monitoring by surveying the literature for control charting techniques suitable for the various types of data available from large programs measuring multiple indicators at multiple locations across various physical environments. Example datasets were obtained for Canada’s Oil Sands Region, including water quality, air quality, facility production and performance, and bird communities, and were analyzed using univariate (e.g., x-bar) and multivariate (e.g., Hotelling’s T²) control charts. The control charts indicated multiple instances of unexpected observations and highlighted subtle patterns in all of the example data. While control charts are not uniquely able to identify potentially relevant patterns in data and can be challenging to apply in some monitoring analyses, this work emphasizes the broad utility of the tools for straightforwardly presenting the results from standardized and routine surveillance monitoring.
Istanbul, hosting millions of tourists annually, faces significant air pollution challenges, impacting both health and climate. The air quality index (AQI) is vital for assessing pollution and its health effects. This study predicts AQI levels in Istanbul using machine learning models (XGBoost and ANN), focusing on pollutants like PM10, NO2, CO, SO2, and O3, with data from 2021-2023. Ensemble methods incorporating meteorological data (temperature, pressure, wind) improved accuracy. Forecasted AQI levels range from “good” to “hazardous,” offering actionable insights. The models achieved low error rates, underscoring their effectiveness in supporting air quality management and sustainable health policies through reliable AQI predictions.
Often information relevant to a decision is summarized in an index number. This paper explores conditions under which conclusions using index numbers are relevant to the decision that needs to be made. Specifically, it explores the idea that a statement using scales of measurement is meaningful in the sense that its truth or falsity does not depend on an arbitrary choice of parameters; the concept that a conclusion using index numbers is useful for the specific decision that needs to be made; and the notion that such a conclusion is legitimate in the sense that it is collected and used in a way that satisfies cultural, historical, organizational, and legal constraints. While meaningfulness is a precisely defined concept, usefulness and legitimacy are not, and the paper explores properties of these concepts that lay the groundwork for making them more precise. Many examples involving two well-known and widely-used index numbers, body mass indices and air pollution indices, are used to explore the properties of and interrelationships among meaningfulness, usefulness, and legitimacy.
In several urban industrial regions affected by air pollution, it is crucial to monitor air quality in order to improve the quality of life and prevent any damage to health. This paper mainly focuses on the prediction of air quality index (AQI) using two different machine learning algorithms SVM and KNN. In recent times, machine learning has become widely popular and relevant in the forecasting of AQI because of its ability to work with large datasets and provide highly accurate conclusions from raw data. This study helps in analyzing several international research works which helps in better understanding of the forecasting power of algorithms used and the predicted outcomes provide insights into the air quality which aid authorities in decision making. Our analysis shows that hybrid models, created using multiple algorithms, outperform traditional models involving a single algorithm. Moreover, the usage of larger datasets to train the models led to more accurate results.
This study compares air quality predictions for a select number of coastal and non-coastal cities. The analysis reveals that coastal cities can experience better air quality due to the ocean's cleaning effect, but this is not always the case. When sewage and other pollutants are dumped into the ocean, it significantly impacts coastal air quality. Non-coastal cities can also experience poor air quality due to factors such as rising temperatures and heavy industry. Advanced machine learning techniques are used to make predictions about air quality using historical data. The study highlights the importance of comprehensive tracer programs and improved coordination between air pollution and boundary layer field observation programs to better understand physical processes affecting dispersion over land, water, and transition zones.
International air quality indexes (AQIs) are derived from air pollution and are essential global tools for mitigating diseases such as asthma, as they are used to reduce exposure to triggers. The aim of this article is to systematically review the global literature on the use of AQIs in asthma-related studies. To evaluate the importance of the variables considered, a citation frequency index (Q) was used. The results suggest that the most frequently reported air pollutants related to asthma are PM (Q3) > NO2 (Q3) > O3 (Q3) > CO (Q3) > NO (Q3) > SO2 (Q3). In addition, climate variables play a relevant role in asthma research. Temperature (Q4) emerged as the most relevant climate variable, followed by atmospheric pressure (Q3) > wind direction (Q3) > solar radiation (Q3) > precipitation (Q3) > wind speed (Q3). AQIs, specifically the U.S.EPA Air Quality Index and the Air Quality Health Index, are directly associated with air pollution and the prevalence, severity and exacerbation of asthma. The findings also suggest that climate change presents additional challenges in relation to asthma by influencing the environmental conditions that affect the disease. Finally, this study provides a comprehensive view of the relationships among air quality, air pollutants and asthma and highlights the need for further research in this field to develop public health policies and environmental regulations.
Comparing the efficiency and accuracy of air quality monitoring methods is an important aspect to ensure the protection of the environment and the health of citizens. In this study, we developed a project to measure air quality in several areas using Internet-connected devices built with Arduino and a standardised device such as AirVisual. To evaluate the accuracy and performance of the two monitoring methods, we collected data including humidity levels, temperature, and PM 2.5 (particulate matter) from both devices. Through the analysis of these data, we compared and evaluated the changes in air quality and the performance of the two methods in real time. The results of our study provide a deep understanding of the compatibility and accuracy of different air quality monitoring methods and contribute to the development of knowledge in this field. This study points out the importance of using IoT technology for air quality monitoring and the opportunities for improving existing monitoring methods.
(1) Background: Environmental risks such as air pollutants pose a threat to human health and must be communicated to the affected population to create awareness, such as via health literacy (HL); (2) Methods: We analyzed HL in the context of environmental health risks, including sources of information and prior knowledge, in a sample from the German general population using Kendall’s rank correlations, regression analyses, and explorative parallel mediation analysis; (3) Results: The survey included 412 German participants aged between 18 and 77. HL was found to be problematic to inadequate. The internet, family and friends, and newspapers were the most frequently cited sources of information. Mobile apps were mostly unknown but were requested by sample subjects. Although subjects expressed environmental concerns and exhibited rather good levels of knowledge, the majority perceived no risk to human health and rated air quality quite positively. Knowledge on particulate matter, the term “ultrafine particles”, and protective measures was found to be rather low. HL was associated with the use of newspapers and commercials as sources of information. The relationship between age and HL is fully mediated by the use of newspapers and information from TV commercials; (4) Conclusion: HL should be promoted by raising awareness of the health effects of environmental pollutants. In particular, the information channels preferred by the affected population should be used and further information opportunities such as apps should be publicized, e.g., through campaigns. An improved HL can assist policy makers in creating a healthier environment by empowering individuals to become more environmentally aware and protect their own health. This, in turn, has the potential to reduce health-related costs.
Hava kirliliğinin zamanla giderek artması insan sağlığını ciddi şekilde tehdit etmektedir. Bu çalışmada Bursa şehrinde 2022 yılında ölçülmüş olan evsel ısınma, ulaşım ve sanayi kaynaklı sebeplerle oluşan hava kirliliği değerleri ile hava kalitesi indeksine göre bu kirliliğin insanları etkileme dereceleri incelenmiştir. Çalışmada Bursa ilinde bulunan Bursa Merkez, Beyazıt ve Kestel Hava Kalitesi İzleme istasyonlarında 2022 yılında ölçümü yapılmış olan çapı 10 µm altında olan Partikül Madde (PM10), çapı 2.5 µm altında olan Partikül Madde (PM2.5), Kükürt dioksit (SO2), Azot dioksit (NO2), Ozon (O3) ve reanaliz rüzgar şiddeti ve yönü verileri kullanılmıştır. Bu değerler Hava Kalitesi İndeksi’ne göre incelendiğinde Bursa İstasyonu için PM10 değerleri 244 gün iyi, 88 gün orta ve 33 gün hassas sınırlarındadır ayrıca bu istasyonda SO2 değerlerinin tamamı iyi aralığındadır. Beyazıt İstasyonu’nda ölçülen NO2 değerlerinin %6.4’ü orta sınıfında iken geri kalanı iyi koşuldadır. Kestel İstasyonu’nda ise PM10 değerleri 352 gün iyi, 11 gün orta ve 2 gün hassas grubunda, SO2 değerleri %3.7’si orta sınıfta geri kalanı iyi koşuldadır. Bu değerler ışığında hava kalitesinin iyileştirilmesi toplum sağlığı açısından çok önemli olmaktadır.
The coastal region of Bangladesh is increasingly suffering from air pollution in recent years. This study represents an analysis of the mean atmospheric column nitrogen dioxide (NO2), Sulphur di oxide (SO2), Ozone (O3), and carbon monoxide (CO) over the coastal area of Bangladesh during 2018-2022 period. Measurements of nitrogen dioxide (NO2), Sulphur di oxide (SO2), Ozone (O3) and carbon monoxide (CO) were obtained from recently launched Sentinel-5 Precursor spacecraft using Tropospheric Monitoring Instrument (Sentinel-5P TROPOMI). The aim of this study is to evaluate and visualize coastal air quality using relatively high-resolution data. PM2.5 was analyzed for the period of 1998-2019 year and the data was collected from the SEDAC website. The NO2, SO2, O3, PM2.5, and CO satellite data were then used in Google Earth Engine (GEE) and ArcGIS 10.7 software for further analysis and retrieval of the atmospheric data. The findings showed significantly high values for Khulna, Jashore, Satkhira, and Narail districts. From the NO2 observation, high values were identified in Chandpur, Shariatpur, and Lakshmipur with a highest average value of 74.2 μmol/m2. It was found that the SO2 and O3 were present significantly high over the Khulna, Bagerhat, Satkhira, and Narail districts. Particulate Matter PM2.5 exceeded BNAAQS value in every year since 1998 all over the coastal Districtswith an average value of 56 μg/m3. Years 2020 and 2021 were found much clean air than the other study years might be due to the Covid-19 Lockdown all over the country. From the trend analysis, it was found that most of the air pollutants showed an increasing trend. The interior coast was found more polluted than the exposed coast. The overall results of this study confirmed the capability of Sentinel-5P TROPOMI data to be used in monitoring the air quality over coastal areas of Bangladesh. This study highlights the widespread problem of air pollution in the coastal districts of Bangladesh and emphasizes the need for immediate action to tackle this issue. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 11(2), 2022, P 53-65
Makkah draws millions of visitors during Hajj and Ramadan, establishing itself as one of Saudi Arabia’s most bustling cities. The imperative lies in maintaining pristine air quality and comprehending diverse air pollutants to effectively manage and model air pollution. Given the capricious and variably spatiotemporal nature of pollution, predicting air quality emerges as a notably intricate endeavor. In this study, we confronted this challenge head-on by harnessing sophisticated machine learning techniques, encompassing the fine decision tree (FDT), ensemble boosted tree (EBOT), and ensemble bagged tree (EBAT). These advanced methodologies were enlisted to project air quality index (AQI) levels, focusing specifically on the Makkah region. Constructed and trained on air quality data spanning 2016 to 2018, our forecast models unearthed noteworthy insights. The outcomes revealed that EBOT exhibited unparalleled accuracy at 97.4%, astutely predicting 75 out of 77 samples. On the other hand, FDT and EBAT achieved accuracies of 96.1% and 94.8%, respectively. Consequently, the EBOT model emerges as the epitome of reliability, showcasing its prowess in forecasting the air quality index. We believe that the insights garnered from this research possess universal applicability, extending their potential to regions worldwide.
Considering the existence of autonomous vehicles, it is seen that many studies have been done on the traffic light classification recently. Automatic determination of traffic lights can significantly prevent traffic accidents. As the number of vehicles on the road increases daily, such a classification process becomes crucial. The classification process appears to result in higher accuracy using deep learning approaches. In this study, a deep learning-based classification process is performed for traffic lights. A convolutional neural network model with efficient parameters is proposed. Additively, hyperparameter adjustment is made. In addition to this, the effects of color spaces and input image sizes on the classification results are investigated. There are four classes of images with red, yellow, green, and off tags in the database used. When the results are examined, it is seen that the classification accuracy of over 96% is achieved.
OBJECTIVE: To review the evidence on health effects of air pollution for the Canadian Smog Advisory Program.
METHODS: Evidence was reviewed by two expert panels, who were asked to define the health effects expected at levels of exposure given by the National Ambient Air Quality Objectives, to examine a variety of issues related to communicating with the public about environmental health risks, and to draft health messages for the advisory program.
RESULTS: The panels concluded that health effects of ground-level ozone at levels that occur in Canada include pulmonary inflammation, pulmonary function decrements, airway hyperreactivity, respiratory symptoms, possible increased medication use and physician/emergency room visits among individuals with heart or lung disease, reduced exercise capacity, increased hospital admissions and possible increased mortality. Similar effects were felt to occur in association with airborne particles, with the exception of inflammatory changes, and with the addition of increased school absenteeism. Poor data on individual exposure were identified as a limitation of studies on hospital admissions and mortality.
RECOMMENDATIONS: The panels identified the need to reflect the evidence accurately without unduly raising public concern and recommended that advisory health messages identify expected health effects, while health care providers could more appropriately recommend protective actions to individuals. Supplementary educational strategies and evaluation of the advisory program were also recommended.
The paper presents the overview of current methods for air quality evaluation, i.e. air stress indices and, especially, air quality indices. Traditional air quality indices are determined as mean values of selected air pollutants. Thus, air quality evaluation depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated e.g. using systems based on fuzzy logic. Therefore, the paper presents a design of air quality indices based on hierarchical fuzzy inference systems. Tree and cascade hierarchical fuzzy inference systems of Mamdani type are proposed as alternative air quality indices. For selected localities, they provide both the resulting class of air quality and the degree of membership to each class.
An air pollution index is a quantitative tool through which air pollution data can be reported uniformly. There have been efforts to describe overall air pollution by an aggregation of pollutant subindices. When ambiguous, these aggregations raise unnecessary alarm by declaring a less polluted air to be highly polluted. Similarly, when eclipsed, a false sense of security is provided by indicating highly polluted air as less polluted. Linear sum and root sum square forms in vogue suffer from ambiguity. Whereas the maximum operator aggregation does not consider change in the remaining pollutants, it is not a good tool for management purposes. In this paper, an ambiguity-and eclipsicity-free function has been presented for aggregation of air pollution subindices. For computer adaptation of the aggregation process, the subindices have been expressed as full range functions of the pollutant concentrations.
Against the background of the growing demand for indices suited to assess the integral air quality that is not restricted to a single air pollutant, formulations for statistical air stress indices and an impact-related air quality index (DAQx) are presented. Their sensitivity depending
on emission and air mass exchange conditions is investigated by test calculations based on air pollution data from three different sites in southwest Germany characterised by different air pollution levels and one site (Szeged) in southern Hungary with a comparatively high air pollution level.
The results can be explained by methodical characteristics of the indices and the local emission situation.
German
Vor dem Hintergrund der steigenden Nachfrage nach Indizes zur Bewertung der integralen Luftqualität, die über die standardmäBige Beurteilung einzelner Luftkomponenten
hinausgeht, werden Ansätze für statistische Luftbelastungsindizes und einen wirkungsbezogenen Luftqualitätsindex (DAQx) vorgestellt. Ihre Sensitivität, die von den Emissionsbedingungen und den Austauschverhältnissen abhängt, wird über Testrechnungen für
drei Standorte im Südwesten Deutschlands mit unterschiedlichem Emissionseinfluss und einen lufthygienisch belasteten Standort (Szeged) im Süden von Ungarn analysiert. Die Unterschiede in den Resultaten lassen sich über methodische Kennzeichen der berücksichtigten Indizes
und die lokale Emissionssituation erklären.
All existing environmental index systems, along with principles for their design, application and structure, are included in this book. Chapter I introduces environmental data, presenting simple communicative approaches such as environmental quality profiles. It also describes the national monitoring activities that generate these data and discusses the difficulty of constructing meaningful environmental damage functions. Chapter II presents a new conceptual framework that is designed to embrace nearly all existing environmental indices, allowing the behavior of different index structures to be compared and probed in detail. Chapter III concentrates on air pollution indices, using the conceptual framework introduced in Chapter II to analyze and compare published air pollution indices. Chapter III also gives a detailed summary of the historical evolution and scientific basis for the Pollutant Standards Index (PSI), which has been developed for uniform application throughout the United States. Computational aids for applying PSI to actual air quality data are included. Chapter IV covers water pollution indices, using the theoretical framework and concepts from Chapter II to examine today's water pollution indices; it also presents design principles for an ideal water quality index and discusses a candidate index structure. In both Chapters III and IV, the current air and water index usage patterns in the United States are described in detail. Finally, Chapter V presents conceptual approaches, such as quality of life and environmental damage functions, that extend beyond the traditional fields of air and water pollution. This book should serve as a basic reference for users wishing to apply indices to analyze environmental data.
Note: Copies of this book are available from Amazon.com. Please enter "Environmental Indices: Theory an Practice" into Google or use the following direct link: http://www.amazon.com/Environmental-Indices-Practice-Wayne-Ott/dp/0250401916
There are many different air quality indexes, which represent the global urban air pollution situation. Although the index proposed by USEPA gives an overall assessment of air quality, it does not include the combined effects (or synergistic effects) of the major air pollutants (Shenfeld, 1970; Ott and Thom, 1976; Thom and Ott, 1976; Murena, 2004). So an attempt is made to calculate the Air Quality Index based on Factor Analysis (NAQI) which incorporates the deficiencies of USEPA method. The daily, monthly and seasonal air quality indexes were calculated by using both these methods. It is observed that a significant difference exists between NAQI and EPAQI. However, NAQI followed the trends of EPAQI when plotted against time. Further, the indexes were used to rank various seasons in terms of air pollution. The higher index value indicates more pollution in relative terms. Moreover, the index may be used for comparing the daily and seasonal pollution levels in different sites.
A daily air pollution index (PI) has been developed and implemented at the urban area of Naples (Italy). Data gathered from nine monitoring stations during 2001–2002 have been analysed and a PI has been developed and applied. The index aims at measuring the status of air pollution with respect to its effect on human health. It can be seen as a modified version of air quality index of Environmental Protection Agency taking into consideration the limit values ruling in Europe. A procedure to evaluate the PI at each monitoring site and on the overall urban area is reported. Additive effects of air pollutants have also been considered and the PI re-evaluated.
This review of the evidence of the health effects of air pollutants focuses on research conducted in Ontario. Seven key Ontario studies are cited. These findings are highly significant for people living in the Great Lakes basin (and particularly the Windsor‐Quebec corridor), where high levels of certain air pollutants (eg, ground‐level ozone and ultra‐fine particles) occur more frequently than in other parts of Canada. The issue is a serious one, requiring an integrated and comprehensive approach by many stakeholders, including the active involvement of organized medicine. It is important that the health effects of these air pollutants are understood. Governments must act to reduce emission levels through statue and regulation bolstered by noncompliance penalties.
The findings of research have included the following: in a Toronto study, a 2% to 4% excess of respiratory deaths were attributable to pollutant levels; children living in rural Ontario communities with the highest levels of airborne acids were significantly more likely to report at least one episode of bronchitis, as well as to show decreases in lung function; and have been linked to increases in pollutants, emergency room visits and hospitalizations in Ontario.
Every Ontarian is affected by air pollutants, although he or she may be unaware of the asymptomatic effects such as lung and bronchial inflammation. This health problem is preventable; while physicians know of the adverse health impacts of air pollution and they are concerned, individually they now focus on the treatment of symptoms. The major recommendations of the report are as follows:
* Enactment of more stringent sulphur and nitrogen oxide emission limits, including a provincewide sulphur dioxide reduction of 75% from current cap levels, and the maximum allowable nitrogen oxides emission limits of 6000 tonnes annually from Ontario Hydro.
* New transportation sector emission limits that should include California‐level standards for light and heavy duty vehicles, reductions from off‐road engines, an expanded vehicle inspection and maintenance program, and tougher standards for sulphur‐in‐fuel content.
* Petitioning the United States Environmental Protection Agency administrator under Section 115 of the United States Clear Air Act to require reductions in the American emission of sulphur dioxide and nitrogen oxides, which damage the health of Canadian residents and their environment.
* Physician advice to patients about the risks of smog exposure, physician support for more health effects research on air pollution, and physician promotion of the development of air pollution‐related health education materials.
The recommendations discussed in this paper will, if acted upon, lead to a significant reduction in the overall burden of illness from air pollutants, especially in children and the elderly. These recommendations have been selected from a review of recommendations made by various authorities, and are those that the OMA feels a particular responsibility to support.
This paper presents an assessment of the air quality for the principal cities in developed and developing countries. Part of the vast and widely dispersed information on air quality that is available at this time on the Internet was compiled, thus making possible a comprehensive evaluation of the tendencies that emerged at the end of the 20th century. Likewise, these values are compared to the air quality thresholds recommended by two international organizations: guideline levels of the World Health Organization (WHO) and limit values of the European Union (EU), in order to determine air quality concentration levels in large cities around the world. The current situation of air quality worldwide indicates that SO(2) maintains a downward tendency throughout the world, with the exception of some Central American and Asian cities. NO(2) maintains levels very close to the WHO guideline value around the world. For particulate matter, it is a major problem in almost all of Asia, exceeding 300 microg/m(3) in many cities. Ozone shows average values that exceed the selected guideline values in all of the analyses demonstrating that it is a global problem. In general, the worldwide trend is to a reduction in the concentrations of pollutants because of the increasingly strong restrictions which local governments and international organizations impose. However, in poor countries and those with low average incomes, concentrations of air pollutants remain high and the trend will be the elevation of their ground levels as they develop, making the problem even worse.
Several studies have reported significant health effects of air pollution even at low levels of air pollutants, but in most of theses studies linear nonthreshold relations were assumed. We investigated the exposure-response association between ambient particles and mortality in the 22 European cities participating in the APHEA (Air Pollution and Health--A European Approach) project, which is the largest available European database. We estimated the exposure-response curves using regression spline models with two knots and then combined the individual city estimates of the spline to get an overall exposure-response relationship. To further explore the heterogeneity in the observed city-specific exposure-response associations, we investigated several city descriptive variables as potential effect modifiers that could alter the shape of the curve. We conclude that the association between ambient particles and mortality in the cities included in the present analysis, and in the range of the pollutant common in all analyzed cities, could be adequately estimated using the linear model. Our results confirm those previously reported in Europe and the United States. The heterogeneity found in the different city-specific relations reflects real effect modification, which can be explained partly by factors characterizing the air pollution mix, climate, and the health of the population.
Standards are available for the assessment of many air pollutants, but there is also a demand for indices that consider more air pollutants and enable an assessment of the air quality conditions in a graduated way. Such indices can be divided into impact-independent air stress indices and impact-related air quality indices. While different approaches for the group of air stress indices do exist, only one air quality index (TLQ) was developed in Germany up to now for the assessment of the air quality conditions related to human well-being and health. By use of test calculations performed with air pollution data from selected air quality monitoring stations in Baden-Wuerttemberg for the period 1996 to 1998 and two extreme weather conditions, sensibility of TLQ, an index on a daily basis, is analysed including the daily air stress index LBI BW.
We analyzed a national data base of air pollution and mortality for the 88 largest U.S. cities for the period 1987–1994, to estimate relative rates of mortality associated with airborne particulate matter smaller than 10 microns (PM10) and the form of the relationship between PM10 concentration and mortality. To estimate city-specific relative rates of mortality associated with PM10, we built log-linear models that included nonparametric adjustments for weather variables and longer term trends. To estimate PM10 mortality dose-response curves, we modeled the logarithm of the expected value of daily mortality as a function of PM10 using natural cubic splines with unknown numbers and locations of knots. We also developed spatial models to investigate the heterogeneity of relative mortality rates and of the shapes of PM10 mortality dose-response curves across cities and geographical regions. To determine whether variability in effect estimates can be explained by city-specific factors, we explored the dependence of relative mortality rates on mean pollution levels, demographic variables, reliability of the pollution data, and specific constituents of particulate matter. We implemented estimation with simulation-based methods, including data augmentation to impute the missing data of the city-specific covariates and the reversible jump Markov chain Monte Carlo (RJMCMC) to sample from the posterior distribution of the parameters in the hierarchical spline model. We found that previous-day PM10 concentrations were positively associated with total mortality in most the locations, with a .5% increment for a 10 μg/m3 increase in PM10. The effect was strongest in the Northeast region, where the increase in the death rate was twice as high as the average for the other cities. Overall, we found that the pooled concentration-response relationship for the nation was linear.
In an attempt to meet the public's needs for information on air quality a variety of indexes have been developed and they continue to evolve. To show the complexity and the diversity of such indices, a variety of current air quality indices are described here and compared in regard to their performance and ability to deliver quality information. A number of characteristics seem desirable for an index: consistency, simplicity, versatility and flexibility. In terms of their ongoing development, an AQI also needs to be useful for forecasting, and the method of calculation needs to be sufficiently flexible to allow for pollutants to be added or subtracted as changes to their health impact are revealed. However, much progress is still to be made, mainly through more careful consideration of the combined impact of multiple pollutants, consideration of low level exposure, and with more timely transfer of usable information to the public.
The methodology of the recently developed Daily Air Quality Index (DAQx) and Long-term Air Quality Index (LAQx) is explained. Both indices consider air pollutants frequently monitored at long-term stations within official air pollution control networks. Therefore, they enable an assessment of the integral air pollution, which reflects the ambient air consisting of a mixture of air pollutants more realistic. Both air quality indices are impact related with respect to people. On the basis of results of extensive investigations in environmental medicine and toxicology, they quantify the impacts of a mixture of air pollutants, which is typical of the ambient air, on well-being and health of people in the form of six index classes and ranges of index values, respectively. To analyse the sensitivity of DAQx and LAQx, air pollutant data for the period 1995-2003 were used. They originate from selected stations within the official air pollution monitoring network in the South-West of Germany, which are characterised by different emission conditions.
Communication of the complex relationship between air pollutant exposure and ill health is essential to an air pollution information system. We propose a novel air pollution index (API) system based on the relative risk of the well-established increased daily mortality associated with short-term exposure to common air pollutants: particulate matter (PM10, PM2.5), sulphur dioxide, ozone, nitrogen dioxide and carbon monoxide.To construct our index system, the total incremental daily mortality risk of exposure to these pollutants was associated with an index value ranging from 0 to 10. The index scale is linear with respect to incremental risk. The index is open ended, although, for convenience, an index of 10 is assigned for exposures yielding indices ⩾10.To illustrate the application of this API system, a set of published relative risk factors are used to calculate sub-index values for each pollutant, in the range of air pollutant concentrations commonly experienced in urban areas. To account for the reality of ubiquitous simultaneous exposure to a mixture of the common air pollutants, the final API is the sum of the normalised values of the individual indices for PM10, PM2.5, sulphur dioxide, ozone, nitrogen dioxide and carbon monoxide. This establishes a self-consistent index system where a given index value corresponds to the same daily mortality risk associated with the combined exposure to the common air pollutants. To facilitate health-risk communication, index values are colour coded and associated with broad health-risk descriptors. The utility of the proposed API is illustrated by applying it to monitored ambient concentration data for the City of Cape Town, South Africa.
Summary - Methods of human-biometeorology have to be applied for the assessment of atmospheric impacts on human beings. Among the human-biometeorological effective complexes two are of great importance in the regional scale: the thermal effective complex and the air quality effective complex. With respect to the air quality effective complex, standards for the assessment of single air pollutants exist worldwide. In addition, approaches for statistical air stress indices and impact-related air quality indices were developed. In this study, based on a five- year air pollutant data set from the downtown of a middle-sized Hungarian city, Szeged, the frequency distribution of the air stress index ASISz is compared with the frequency distribution of the new air quality index DAQx. Both indices were developed by German researchers and are on a daily basis. The varying forms of both frequency distributions are mainly caused by the impact-related concentration ranges of single air pollutants, which are typical of air quality indices. Especially carbon-monoxide and PM10 have a stronger influence on the determination of values of air quality indices.
There are many different air pollution indexes which represent the global urban air pollution situation. The daily index studied here is also highly correlated with meteorological variables and this index is capable of identifying those variables that significantly affect the air pollution. The index is connected with attention levels of NO2, CO and O3 concentrations. The attention levels are fixed by a law proposed by the Italian Ministries of Health and Environment. The relation of that index with some meteorological variables is analysed by the linear multiple partial correlation statistical method. Florence, Milan and Vicence were selected to show the correlation among the air pollution index and the daily thermic excursion, the previous day's air pollution index and the wind speed. During the January–March period the correlation coefficient reaches 0.85 at Milan. The deterministic methods of forecasting air pollution concentrations show very high evaluation errors and are applied on limited areas around the observation stations, as opposed to the whole urban areas. The global air pollution, instead of the concentrations at specific observation stations, allows the evaluation of the level of the sanitary risk regarding the whole urban population.
An approach to assess and represent air quality status through an Air Quality Index (AQI), in major metropolitan cities, where different types of activities, viz. industrial, commercial and residential are in progress, on a short and a long term basis is presented. To make the index more informative, air quality status is classified into five different categories, viz. Clean, Moderate, Poor, Bad and Dangerous. Long term air quality indices are then calculated for four metropolitan cities and one city, which is fast developing each representing the different climatological features in India viz. Mumbai, Delhi, Calcutta, Chennai and Nagpur. The air quality index is calculated according to national standards stipulated for different pollutants and zones.
An Air Quality Index is defined as a single term, usually a number, used to describe the degree of contamination of the ambient air. The concept of an air quality index is not new and such an index could be quite useful provided that the index does not over-simplify the situation nor cover up gaps in our knowledge. Some basic information on an assortment of air pollution indicators that have been used nationally and internationally either for public information and/or alert systems is provided.In most of these indicators, aerosols, usually measured by light absorption techniques, and sulphur dioxide, were the pollutants incorporated into the index. Other pollutants used less frequently included carbon monoxide, oxides of nitrogen, hydrocarbons and oxidants.After listing the requirements of a national air quality index a second list of items is presented which would negate the validity of any comprehensive nation-wide index. It is thus concluded that considering the present state of our knowledge and vastly different geographical, topographical and meteorological areas of a country such as Canada the development of a nation-wide air quality index is not recommended.
Synthetic indices are often used to condense complex situations into a single figure. However, this condensing process risks losing potentially useful information, especially when the index is to be utilised by public decision-making bodies. The present study proposes a general strategy, combining a number of different methods, designed to recover information from air-quality indices: graphical methods to reconstruct the composition of pollution, multinomial logit analysis to study the influence of meteorological covariates on air-quality indices, and finally, a probability distribution for the index itself as a basic tool with which to interpret the index's crucial values. Copyright © 2007 John Wiley & Sons, Ltd.
Interest in air quality indices has been increasing in recent years. This is strictly connected with the development and the easy availability of web-communication and on-line information. By means of web pages it is indeed possible to give quick and easy-to-consult information about air quality in a specific area. We propose a class of air quality indices which are simple to read and easy to understand by citizens and policy-makers. They are constructed in order to be able to compare situations that differ in time and space. In particular, interest is focused on situations where many monitoring stations are operating in the same area. In this case, which occurs frequently, air pollution data are collected according to three dimensions: time, space and type of pollutant. In order to obtain a synthetic value, the dimensions are reduced by means of aggregation processes that occur by successively applying some aggregating function. The final index may be influenced by the order of aggregation. The hierarchical aggregation here proposed is based on the successive selection of order statistics, i.e. on percentiles and on maxima. The variety of pollutants measured in each area imposes a standardization due to their different effects on the human health. This evaluation comes from epidemiological studies and influences the final value of the index. We propose to use simultaneously more than one index of the selected class and to associate a measure of variability with every index. Such measures of dispersion account for very important additional information. Copyright © 2002 John Wiley & Sons, Ltd.
Observational epidemiological studies have had an important role in understanding the public health impacts of air pollution.
In such studies, accurate assessment of exposure remains a major challenges, especially in studies involving large populations.
Here we review state-of-the-art approaches to assessment of population exposure in epidemiological studies with a focus on
approaches applied in the Border Air Quality Study (www.cher.ubc.ca⋏qs.htm). The strengths and limitations of these methods
are discussed and future research needs identified.
KeywordsAir quality-epidemiology-exposure assessment-health effects-land use regression-vehicle emissions-wood smoke
This paper develops an index of pollution based on the epidemiological dose-response function associated with each pollutant, and the welfare losses due to exposure to pollution. The probability of damage is translated into welfare losses, which provides the common metric required for aggregation. Isopollution surfaces may then be used to compare environmental quality over time and space. An Air Pollution Index (API) is computed using 1997 data for the criteria pollutants under the Clean Air Act (CAA). The results are compared with the EPA's Pollutant Standards Index (PSI). Two significant differences emerge: unlike the PSI, the API facilitates a detailed ranking of regions by air quality and API values may contradict PSI results. Some regions with PSI values of 100–200 are considered less polluted under the proposed methodology than those with PSI values between 50 and 100. The key reason for the difference is that PSI values are determined entirely by the gas with the highest relative concentration whereas the API value is based on the ambient concentrations of all pollutants.
National and international authorities recommend a variety of air-quality standards that should not be exceeded in local and regional scales currently. With this work a uniform indexing scale is introduced which characterises several urban pollutants in a simple and comparable manner. The “indicators” proposed are implemented at the Athens Metropolitan Area (AMA) which is an area with serious pollution problems. Hourly data from all available monitoring stations are analysed during 1983 and 1995. This analysis demonstrates that the status of air quality in Athens can be characterised as acute with regards to photochemical pollutants while strong spatial and temporal variability is encountered for all pollutants.
Based on the concept of "loss of information", this paper presents an objective measure that may be used to compare different aggregating methods for constructing a composite environmental index (CEI). Using the proposed measure, three multiple attribute decision making methods are evaluated and compared through an empirical example and two related simulation studies. The main findings, which demonstrate the effectiveness of the proposed measure, are presented. (c) 2005 Elsevier B.V. All rights reserved.
The Environmental Protection Administration of Taiwan has provided air quality service by reporting the pollutant standard index (PSI) since 1997. This standard, developed by the USA Environmental Protection Authority, compares concentrations of the five main common pollutants (PM10, O3, SO2, CO, NO2). For each pollutant, a sub-index was calculated from a segmented linear function that transforms ambient concentrations onto a scale extending from 0 to 500. The standard index is based on the highest sub-index. The main disadvantage of the PSI is that it only identifies the levels of one pollutant at a time. Hence it cannot show whether more than one pollutant exceeds the daily standard level. A region may be regarded as polluted when the PSI scale reaches 100; however, this transformation can be misleading. For example, when the standard level for PM10 concentration reaches 125 μg/m3, it converts to a less significant value of 88 on the PSI scale. Confusion can also arise because the standard pollution concentration level varies among different countries. This paper discusses a more effective way of determining a suitable concentration level of pollutants in Taiwan.Combining the original PSI with an entropy function, we can develop a revised air quality index (RAQI). The revised version can rectify the current deficiencies of the PSI. It considers the association of the five pollutants, and has the comparative index function. According to tentative results, RAQI should be representative, supplying the public with a better indicator of air quality.
Several concepts and indicators exist to measure and rank urban areas in terms of their socio-economic, infrastructural, and environment-related parameters. The World Bank regularly publishes the World Development Indicators (WDI), and the United Nations reports the City Development Index (CDI) and also ranks megacities on the basis of their population size. Here, we evaluate and rank megacities in terms of their trace gas and particle emissions and ambient air quality. Besides ranking the megacities according to their surface area and population density, we evaluate them based on carbon monoxide (CO) emissions per capita, per year, and per unit surface area. Further, we rank the megacities according to ambient atmospheric concentrations of criteria pollutants, notably total suspended particles (TSP), sulfur dioxide (SO2), and nitrogen dioxide (NO2). We propose a multi-pollutant index (MPI) considering the combined level of the three criteria pollutants (i.e., TSP, SO2, and NO2) in view of the World Health Organization (WHO) Guidelines for Air Quality. Of 18 megacities considered here 5 classify as having “fair” air quality, and 13 as “poor”. The megacities with the highest MPI, Dhaka, Beijing, Cairo, and Karachi, most urgently need reduction of air pollution.
In this paper we discuss air quality assessment in three Italian, German and Polish regions using the index methodology proposed in Bruno and Cocchi (2002, 2007). This analysis focuses first on the local air quality situation of each considered country and then adopts a more general approach with a comparing purpose in terms of pollution severity and toxicity. This is interesting in a global European perspective where all countries are commonly involved in assessing air quality and taking proper measures for improving it. In this context, air quality indexes result to be a powerful data-driven tool which are easily calculated and summarize a complex phenomenon, such as air pollution, in promptly understandable indicators. In particular, the main objective of this work is to evaluate the index performances in discriminating different air pollution patterns. This kind of analysis can be particularly useful, for example, in the perspective of constructing an indicator of air pollution.
Although there are tendencies to develop a single common index which would describe an overall air quality status within an area, constructed from a choice of measurements of individual pollutants, indices describing individual pollutants themselves have several potentials which can be used in ways which are not possible with pollutant concentrations. On the case of Belgrade, Serbia, we investigated possibilities of using such indices for comparisons between pollutants, characterization of monitoring sites, and extending their use to include elements of population exposure. A methodology of adjusting the results obtained at monitoring stations located in severe pollution conditions, like street canyons, is proposed and used.
To determine the effectiveness of the Canadian Smog Advisory Program.
Telephone interviews covering perceptions, knowledge, recall of and response to advisories, and general views on their usefulness and effectiveness were conducted with 1,474 randomly selected individuals in four geographic areas. Multiple logistic regression was used to model advisory recall as a function of explanatory variables.
Recall of advisories was higher (72%; 95% CI 68-77%) when measured immediately following an advisory in southern New Brunswick. Recall was lower when measured at the end of the smog "season", and varied significantly between Toronto (46%; 42-51%), Haldimand-Norfolk (18%; 14-24%) and Vancouver (61%; 56-65%). Education and geographic area (urban versus rural) were the strongest explanatory variables in two final multiple logistic regression models. A minority of those who recalled an advisory reported taking action.
Smog advisories were partially successful in generating awareness, but only marginally effective in promoting action.
It is very useful for the authorities and the people to have daily easy understandable information about the levels of air pollution and the proper measures to be taken for the protection of human health. In this paper we develop an aggregate Air Quality Index (AQI) based on the combined effects of five criteria pollutants (CO, SO2, NO2, O3 and PM10) taking into account the European standards. We evaluate it for each monitoring station and for the whole area of Athens, Greece, an area with serious air pollution problems. A comparison was made with a modified version of Environmental Protection Agency/USA (USEPA) maximum value AQI model adjusted for European conditions. Hourly data of air pollutants from 4 monitoring stations, available during 1983-1999, were analysed for the development of the proposed index. The analysis reveals the Athenian population exposure reaches high levels and during last years a gradual increase of days with unhealthy conditions was detected. The proposed aggregate model estimates more effectively the exposure of citizens comparing with the modified USEPA maximum value model, because counts the impact of all the pollutants measured. Towards the informing and protection of the citizens in an urban agglomeration this model advantages as a political and administrative tool for the design of abatement strategies and effective measures of intervention.
The EU directives on air quality force member states to inform the public on the status of the ambient air quality. The Internet is commonly used for this purpose and often air quality is being presented as an index ranging from good to bad. A review of existing websites and air quality indices shows that the way air quality is interpreted differs considerably. The paper presents a new air quality index. The index is part of a project to develop a website dedicated to comparing air quality in European cities. The common air quality index (CAQI) is not aimed at replacing existing local indices. The CAQI is a set of two indices: one for roadside monitoring sites and one for average city background conditions. Differentiating between roadside and general city conditions is a first step in assuring consistence in the parameters that are being compared.
Air quality indices currently in use have been criticized because they do not capture additive effects of multiple pollutants, or reflect the apparent no-threshold concentration-response relationship between air pollution and health. We propose a new air quality health index (AQHI), constructed as the sum of excess mortality risk associated with individual pollutants from a time-series analysis of air pollution and mortality in Canadian cities, adjusted to a 0-10 scale, and calculated hourly on the basis of trailing 3-hr average pollutant concentrations. Extensive sensitivity analyses were conducted using alternative combinations of pollutants from single and multipollutant models. All formulations considered produced frequency distributions of the daily maximum AQHI that were right-skewed, with modal values of 3 or 4, and less than 10% of values at 7 or above on the 10-point scale. In the absence of a gold standard and given the uncertainty in how to best reflect the mix of pollutants, we recommend a formulation based on associations of nitrogen dioxide, ozone, and particulate matter of median aerodynamic diameter less than 2.5 microm with mortality from single-pollutant models. Further sensitivity analyses revealed good agreement of this formulation with others based on alternative sources of coefficients drawn from published studies of mortality and morbidity. These analyses provide evidence that the AQHI represents a valid approach to formulating an index with the objective of allowing people to judge the relative probability of experiencing adverse health effects from day to day. Together with health messages and a graphic display, the AQHI scale appears promising as an air quality risk communication tool.
We analyzed a national data base of air pollution and mortality for the 88 largest U.S. cities for the period 1987-1994, to estimate relative rates of mortality associated with airborne particulate matter smaller than 10 microns (PM,,) and the form of the relationship between PM10 concentration and mortality. To estimate city-specific relative rates of mortality associated with PM,,, we built log-linear models that included nonparametric adjustments for weather variables and longer term trends. To estimate PM, mortality dose-response curves, we modeled the logarithm of the expected value of daily mortality as a function of PM10 using natural cubic splines with unknown numbers and locations of knots. we also developed spatial models to investigate the heterogeneity of relative mortality rates and of the shapes of PM10 mortality dose-response curves across cities and geographical regions. To determine whether variability in effect estimates can be explained by city-specific factors, we explored the dependence of relative mortality rates on mean pollution levels, demographic variables, reliability of the pollution data, and specific constituents of particulate matter. We implemented estimation with simulation-based methods, including data augmentation to impute the missing data of the city-specific covariates and the reversible jump Markov chain Monte Carlo (RJMCMC) to sample from the posterior distribution of the parameters in the hierarchical spline model. We found that previous-day PM,, concentrations were positively associated with total mortality in most the locations, with a .5% increment for a 10 mug/m(3) increase in PM10. The effect was strongest in the Northeast region, where the increase in the death rate was twice as high as the average for the other cities, Overall, we found that the pooled concentration-response relationship for the nation was linear.
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