Search Results (15)

Climate conditions in combination with the concentration of pollutants increase the human health stress and exacerbate systemic diseases. The city of Rhodes is a desirable tourist destination that is located in a sensitive climate region of the southeastern Aegean Sea in the Mediterranean region. In this work, hourly recordings from a mobile air quality monitoring system, which is located in an urban area of Rhodes city, are employed in order to measure the concentration of regulated pollutants (${\mathrm{S}\mathrm{O}}_2,{\mathrm{N}\mathrm{O}}_2,{\mathrm{O}}_3,{\mathrm{P}\mathrm{M}}_{10} \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{P}\mathrm{M}}_{2.5})$ and meteorological factors (pressure, temperature, and relative humidity). The air quality health index (AQHI) and the discomfort index (DI) are calculated to study the impact of air quality and meteorological conditions on human health. The analysis is conducted during a hot summer period, from 29 June to 14 July 2024. During the second half of the studied period, a heatwave episode occurred that affected the bioclimatic conditions over the city. The results show that despite the fact that the concentration of pollutants is lower than the pollutant thresholds (according to Directive 2008/50/EC), the AQHI and DI conditions degrade significantly over the heatwave days. In particular, the AQHI is classified in the “Moderate” class, and the DI indicates that most of the population suffers discomfort. The AQHI and DI simultaneously increase during the days of the heat episode, showing a possible negative synergy for the health risk. Finally, both the day maximum and night minimum temperature are increased (about 0.8 and 0.6 °C, respectively) during the heatwave days as compared to the whole studied period. Full article

Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO₂, O₃, PM_2.5, and PM₁₀, which were also used as input to a health risk communication protocol known as Air Quality Health Index (AQHI). CO₂ was also measured simultaneously. The study aimed to assess the relationship between indoor pollutant concentrations and AQHI levels with those outdoors and to evaluate the efficacy of building operating practices in protecting students from pollution exposure. The results indicate that the regular air quality monitoring stations and outdoor pollutant levels at schools exhibit similar patterns. School AQHI levels indoors were generally lower than those outdoors, with PM₁₀ levels showing a larger proportional contribution to the calculated values indoors. NO₂ levels in one school were in excess of outdoor values. CO₂ monitored in classrooms commonly exceeded indoor guidelines, suggesting poor ventilation. One school that employed air filtration had lower indoor PM concentrations compared to other schools; however, they were still similar to those outdoors. O₃ levels indoors were consistently lower than those outdoors. This study underscores the utility of on-site, sensor-based monitoring for assessing the health impacts of indoor and community exposure to urban air pollutants. The findings suggest a need for improved ventilation and more strategic air intake placement to enhance indoor air quality. Full article

This paper presents a new framework, “colours-of-the-wind” (COLD), which is designed to estimate air quality based on images from meteorological cameras, data analytics techniques, and the application of deep learning. Existing air quality estimation systems mainly rely on physical monitoring stations, which are limited by the monitoring stations’ physical constraints. Instead of collecting data from environmental monitoring stations, COLD collects air quality data from meteorological cameras. This approach can collect data from any location where a camera could capture a reliable image, which is otherwise not collectable by the physical environmental monitoring station(s). This approach can also avoid bias due to the location of data collection. The system is evaluated by building a prototype based on the Air Quality Health Index from the Hong Kong Observatory. This is one of the unique data-analytic frameworks based on such air quality measurement. The COLD’s air quality estimation is also based on AQHI, the first machine learning-based estimation framework that generates AQHI as the proposed output. Experimental results suggest that the approach adopted by the COLD prototype is feasible and has some promising outcomes. The results also suggest possible parameters for the CNN model used for the training and analyses of the images. Full article

The increased concentration of pollutants is a challenge to the health of the population. This work aims to investigate the health risk that is related to the pollutants’ level in the center of Rhodes city. Rhodes Island is a desirable tourist destination with important economic activity over the southeastern Aegean Sea. This analysis covers the (summer) July–August months and the (winter) December month of 2022. Hourly recordings of the concentrations of PM_2.5, NO₂ and O₃ from a mobile air quality monitoring system (AQMS) are analyzed. In order to investigate the effects of pollution level on people’s health, the Air Quality Health Index (AQHI) is calculated. Results show that summer shows an increased health danger compared to winter period, possibly due to increased traffic emissions, tourist density and the different meteorological conditions. In the summer period, the AQHI is classified between the middle and upper-medium health risk class. During the winter month, AQHI is mainly classified in the low-medium health risk class. The summer shows increased health risk despite the AQHI diurnal variability being lower when compared to December. Additionally, the diurnal differences between the two periods show an increased health risk in the summer period for the majority of the hours. Finally, this analysis shows that traffic activities possibly affect the health risk and also highlights that the authorities should adopt green policies to protect human health and the environment. Full article

One of people’s greatest concerns about air quality degradation is its impact on human health. This work is a case study that aims to investigate the air quality and the related impact on people’s health in a coastal city over the eastern Mediterranean. The analysis proceeded during a low-tourist density period, covering the days from 17 to 27 November 2022. Hourly ${\mathrm{PM}}_{2.5}$, ${\mathrm{NO}}_2$ and ${\mathrm{O}}_3$ concentration records from three, mobile, Air Quality Monitoring Systems (AQMS), established in an urban location, port and central area of Rhodes city, are analyzed. To investigate the impact of pollution levels on human health, the Air Quality Health Index (AQHI) is calculated. The daily and diurnal variation of pollutants’ concentration and AQHI among the different areas, as well as the relation among the ambient air pollutants and AQHI, are studied. Additionally, to investigate the impact of wind regime on the variation of pollution and AQHI levels, the hourly zonal and meridional wind-speed components, as well as the temperature at 2 m, the dew point temperature at 2 m, and the height of the boundary layer from ERA5 reanalysis, are retrieved for the region of the southeastern Mediterranean. Results show that the highest pollution level occurs in the city center of Rhodes, compared to the rest of the studied locations. In general, the findings do not show exceedances of the pollutants’ concentration according to the European Directive 2008/50/EC. Moreover, findings show that in some cases, the health risk is classified from Low to Moderate in terms of AQHI. The analysis indicates that the climate conditions affect the pollutants’ concentration due to dispersion, and likely, the atmospheric transport of pollutants. Finally, this work aims to improve the knowledge regarding the air quality of southeastern Greece, promoting the framework for the green and sustainable development of the South Aegean Sea. Full article

Accurate assessments of exposure to urban air pollution with higher traffic emissions and its health risks still face several challenges, such as intensive computation of air pollution modeling and the limited availability of personal activity data. The macroscopic health effects can be transmitted to the whole population for personal prevention via air quality health index (AQHI), but the possibility risk index of the specific allergic diseases is still lacking. This interdisciplinary study aims at evaluating the forecasted results of high-resolution air quality with updated traffic emissions and accessing the potential impacts of outdoor pollution on morbidity of rhinitis for urban residents. A high-resolution modelling system (1 km × 1 km) containing the online traffic emission model (VEIN), meteorological and air quality model (WRF-CHIMERE) and the health impact module was developed. A new health index of Potential Morbidity Risk Index (PMRI) was further established using higher resolution health risk coefficients of major air pollutants on allergic rhinitis, and different methods (with/without considering population distributions) targeting different user groups (residents, hospitals and health administrations) were calculated and analyzed. Operational forecasted results of hourly PMRI can be further combined with online map services to serve as an effective tool for patients with allergic rhinitis to arrange their daily activities so as to avoid acute exacerbation. The forecasted PMRIs accessible to the public will also be beneficial for the public health administrations in planning the medical resource and improving the outpatient efficiency. Full article

An Air Quality Health Index (AQHI), a health risk-based air pollution index, was constructed to communicate to the public their health risks due to exposure to air pollution in Bangkok, Thailand. This AQHI was built by analyzing the association between total excess respiratory disease-related deaths and individual air pollutants, using a time-series analysis of daily data from 2010 to 2019. We used Poisson regression in a generalized additive model, with natural cubic smooth splines to analyze the data and controls for other common variables (time, temperature, relative humidity, day of the week, and public holidays). The regression coefficients of these variables were then employed to establish the suitability of this AQHI for Bangkok. The results indicated that a 10-unit increase in particulate matter (PM₁₀), fine particulate matter (PM_2.5), ozone (O₃), or nitrogen dioxide (NO₂) was statistically associated with increased respiratory disease mortality. The coefficients of these four pollutants were then adopted in the construction of an AQHI for Bangkok. Compared with the currently used Air Quality Index (AQI), the AQHI was a more effective indicator in communicating multiple air pollution-related health risks to the public in Bangkok. Full article

Air pollution places a considerable disease burden on the public. Compared with the widely applied air quality index (AQI), the air quality health index (AQHI) provides a more comprehensive measure of multiple pollutants. In this study, AQHI was constructed using environmental data, meteorological data, and the daily mortality data of Beijing residents from 2018 to 2020. Factors increasing the susceptibility of the population to the health effects of air pollution were identified to aid the construction of a specific AQHI (S-AQHI) for susceptible populations. The findings indicated that older adults, women, and people with respiratory disease are more susceptible to the short-term health effects of air pollution. The relative deviation in the AQHI and S-AQHI for changes in daily mortality percentage of various specific populations ranged from only 1.4% to 10.3%, indicating the universality of the AQHI in its capacity to predict health risks. The Spearman coefficient of correlation between the AQHI and AQI was 0.78 (p < 0.01). Each increase in the interquartile range of the AQHI and AQI results in an increase of 1.894% and 1.029% in the total daily mortality, respectively, demonstrating the stronger capacity of the AQHI to predict daily mortality compared to the AQI. Full article

This study focused on investigating possible associations between exposure to urban air pollution and the number of emergency department (ED) visits for various health outcomes. The outcomes were grouped into four chapters of the International Classification of Diseases Tenth Revision (ICD-10) system (i.e., Chapter II-IV: “Neoplasms”, “Diseases of the blood”, “Endocrine, nutritional and metabolic diseases”, and XVIII: “Symptoms, signs and abnormal clinical and laboratory findings“). The data were collected for the city of Toronto, Canada, (2004–2015, 4292 days). Four gaseous air pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), ground level ozone (O₃), and sulfur dioxide (SO₂)) and fine particulate matter (PM_2.5), and two calculated air quality health indexes (AQHI) based on Toronto were used. The statistical models were constructed by applying the conditional Poisson regression. The exposure was assessed over a maximum of 15 days (time lags 0–14 days). An analysis was performed with the following strata: sex, age, and seasons. Relative risks (RR) and their 95% confidence intervals (95%CI) were estimated for an increase in concentration by a one interquartile range (IQR). For the AQHI (composed of NO₂, O₃, and PM_2.5), IQR = 1, the estimations for lag 1 and all patients, are RR = 1.023 (95%CI: 1.008, 1.038), 1.026 (1.012, 1.040), 1.013 (1.003, 1.024), and 1.007 (1.003, 1.010) for Chapters II–IV and XVIII, respectively. The results show that in the four large, analyzed health groups, the impact of air quality mainly occurs over a short period (from current day to a maximum of 3 days after exposure). Full article

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO₂), ground level ozone (O₃), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada. Full article

Air Quality Health Index (AQHI) is a scale designed in Canada to help residences understand the impact of air quality on health. This study investigated temporal variability of daily AQHI and impact of regional transport on AQHI in Windsor, Ontario, Canada from 2016 to 2019. The four-year average daily AQHI was 2.9, slightly below the upper limit of the low health risk level of 3. AQHI value decreased from 2.95 to 2.81 during the study period, indicating the improvement of air quality. Half of the days, AQHI were 3 regardless of season. AQHI was higher in the warm season (3.1) than in the cold season (2.6) due to more frequent moderate risk days (27%, AQHI = 4) in warm season and more frequent low risk days (42%, AQHI = 2) in the cold season. Among the three pollutants considered, O₃ was the most frequently reported dominant contributor to daily AQHI (88% of days), followed by NO₂ (12%), especially in the cold season, with small contribution from PM_2.5 (<1%). Trajectory analysis found that AQHI ≤ 3 days were closely associated with air masses from the north and northwest, whereas AQHI > 3 days were closely associated with air masses from the west and southwest. This is because northerly flows brought in clear air mass owing to less industrial facilities. Polluted air masses were transported from the south of Windsor, where several industrial states of the US were located. Directional AQHI resembles O₃ more than NO₂ or PM_2.5 concentrations do. Further improvement of AQHI in Windsor could be challenging because O₃ concentrations have continued to increase in recent years. Thus, more effective control measures to mitigate O₃ pollution are warranted to reduce its impact on human health and the environment. Full article

Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision-making. Several important pollutants, such as SO₂, CO, PM₁₀, O₃, NOx, H₂S, location, and many others, have important effects on air quality. Air quality should be recorded and measured based on the total effect of pollutants that are collectively prescribed by a numerical value. In Canada, the Air Quality Health Index (AQHI) is used which is one numerical value based on the total effect of some concentrations. Therefore, evolution is required to consider the complex, ill-defined air pollutants, hence several naive and noble approaches are used to study AQHI. In this study, three approaches such as hybrid data-driven ANN, nonlinear autoregressive with external (exogenous) input (NARX) with a neural network, and adaptive neuro-fuzzy inference (ANFIS) approaches are used for estimating the air quality in an urban area (Jeddah city—industrial zone) for public health concerns. Over three years, 1771 data were collected for pollutants from 1 June 2016 until 30 September 2019. In this study, the Levenberg-Marquardt (LM) approach was employed as an optimization method for ANNs to solve the nonlinear least-squares problems. The NARX employed has a two-layer feed-forward ANN. On the other hand, the back-propagation multi-layer perceptron (BPMLP) algorithm was used with the steepest descent approach to reduce the root mean square error (RMSE). The RMSEs were 4.42, 0.0578, and 5.64 for ANN, NARX, and ANFIS, respectively. Essentially, all RMSEs are very small. The outcomes of approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of artificial intelligent techniques, the public’s early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed notable performance of NARX with a neural network, ANN, and ANFIS-based AQHI model for high dimensional data assessment. Full article

The Air Quality Health Index (AQHI) is a tool that has been developed in order to address the health effects caused by simultaneous exposure to several different air pollutants. Short-term health effects in terms of mortality or morbidity are used in order to construct an index. In this study, different indexes for different health outcomes, based on the concentrations of NO₂, O₃, and PM₁₀ at an urban background measuring station in Stockholm during the period of 2015–2017, are calculated by using different risk-coefficients obtained from a meta-analysis. An AQHI based on local risk-coefficients for asthma emergency department visits (AEDV) in Stockholm is also included in the analysis. Correlation coefficients between different pairs of AQHIs, where the additive effects associated with exposure to NO₂, O₃, and PM₁₀ during 2015–2017 are used, exhibit R-values as in 12 out of 15 cases exceed 0.80. However, the average risk increase for different AQHIs are very different, where indexes based on hospital admissions for asthma are larger than those based on mortality outcomes. An overall conclusion is that different AQHIs for different population groups are not needed, but the index may need to be weighted differently for different population groups. Full article

In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NO_x, O₃, PM₁₀ and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m^–3 increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NO_x, O₃ and PM₁₀, respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m^–3. In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM₁₀ is somewhat smaller than the mean value of the meta-coefficient, while for O₃, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NO_x as an indicator of AEDV, but compared to the mean value associated with NO₂, our value of NO_x is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NO_x exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O₃ contributes to an increase in AQHI during the spring. For PM₁₀, there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM₁₀ with 3.1%, followed by O₃ with 2.0%. Full article

This study presents the development and evaluation of a next generation air monitoring system with both laboratory and field tests. A multi-parameter algorithm was used to correct for the impact of environmental conditions on the electrochemical sensors for carbon monoxide (CO) and nitrogen dioxide (NO₂) pollutants. The field evaluation in an urban roadside environment in comparison to designated monitors showed good agreement with measurement error within 5% of the pollutant concentrations. Multiple sets of the developed system were then deployed in the Hong Kong Marathon 2015 forming a sensor-based network along the marathon route. Real-time air pollution concentration data were wirelessly transmitted and the Air Quality Health Index (AQHI) for the Green Marathon was calculated, which were broadcast to the public on an hourly basis. The route-specific sensor network showed somewhat different pollutant patterns than routine air monitoring, indicating the immediate impact of traffic control during the marathon on the roadside air quality. The study is one of the first applications of a next generation sensor network in international sport events, and it demonstrated the usefulness of the emerging sensor-based air monitoring technology in rapid network deployment to supplement existing air monitoring. Full article