Explore the vast range of titles available.

Air pollution is a major problem in the modern world; although it particularly impacts developing countries, which are experiencing fast and often uncontrolled industrialization, its effects constitute a global burden on the environment and health. At the same time, the costs of effective air quality monitoring programs are prohibitive for emerging economies, thus making any correction difficult to assess. Emerging technologies, such as distributed networks of sensors organized in the Internet of Things, are under the lens of scientific and industrial communities as a valuable, low-cost alternative to standard techniques. In this paper, we report a review of current approaches to distributed air quality monitoring. Specifically, we (1) emphasize the role of crowdsensing in leveraging sensor-enabled mobile devices for large-scale environmental data collection and (2) discuss criticalities, open challenges, and future perspectives in enforcing data security when such approaches are deployed in real application scenarios.

The article evaluates air pollution by particulate matter (PM) in indoor and outdoor air in one of the Polish health resorts, where children and adults with respiratory diseases are treated. The highest indoor PM concentrations were recorded during the winter season. Therefore, the maximum average daily concentration values in indoor air for the PM 10 , PM 2.5 , and PM 1 fractions were 50, 42 and 23 µg/m 3 , respectively. In the case of outdoor air, the highest average daily concentrations of PM 2.5 reached a value of 40 µg/m 3 . The analyses and backward trajectories of episodes of high PM concentrations showed the impact of supra-regional sources and the influx of pollutants from North Africa on the variability of PM concentrations. The correlation between selected meteorological parameters and PM concentrations shows the relationship between PM concentrations and wind speed. For example, the correlation coefficients between PM 1 (I) and PM 1 (O) concentrations and wind speed were − 0.8 and − 0.7 respectively. These factors determined episodes of high PM concentrations during winter periods in the outdoor air, which were then transferred to the indoor air. Elevated concentrations in indoor air during summer were also influenced by chimney/gravity ventilation and the appearance of reverse chimney effect.

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.

NO2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

Background . Air pollution exposure has a detrimental effect on children who spend more than 17% of their weekdays inside a school building. The purpose of this study is to look into the effects of particulate matter (PM) and toxic gases on health of the school children. Between April and November 2018, samples were collected in real time from ten different schools (both indoor and outdoor) over four hours on two consecutive days at each school. During the first two hours, when students were present in the classroom, measurements were conducted inside the classroom. After that the measurements were conducted outside the classroom but within the school premises - when students were playing on the playground or eating breakfast outside of classroom. Method . To evaluate the impact of air pollution, 250 students (on average 20 students from each school) aged from 9 to 12 were selected from ten schools. Automatic monitors (AEROCET 531S, USA) were employed to measure PM 1.0 , PM 2.5, and PM 10 concentrations. NO 2 , TVOC, and CO 2 concentrations were measured using an AEROQUAL (500S, New Zealand), and the respiratory rate is measured by BSMI Peak Flow Meter (Made: BSMI, Origin: China). Monitors were placed at about 2.0 meters above the floor at breathing height and no student wore the sensors. The ANOVA test was conducted to see the statistical significance between air quality parameters and peak flow meter readings. Results . The mean ± standard deviation of PM 1.0 , PM 2.5, and PM 10 concentrations were 19.1 ± 3.6, 34.2 ± 10.1, and 131.3 ± 58.6 μ gm −3 , respectively. PM 2.5 and PM 10 concentrations exceeded WHO standards (15 and 45 μ gm −3 of 24 h) by 2.3 and 2.9 times. The highest concentrations of toxic gases were found on school campuses where vehicle densities (measured manually) were high. The mean Hazard Quotient (HQ) for PM 10 (2.5 ± 2.2 indoor; 3.6 ± 2.6 outdoor) and PM 2.5 (1.8 ± 0.8 indoor; 1.9 ± 1.0 outdoor) among all participating students was >1 indicating an unacceptable risk for human health. Lung function associated with the PEF value has a negative correlation with PM 1.0 and PM 2.5 concentrations in most cases. Conclusions . The findings of this study are useful in gaining a general understanding of the school environment in Dhaka. It aimed to understand how children were personally exposed in school and to develop effective control strategies to mitigate negative effects.

From a public health perspective, it is necessary to improve indoor air quality (IAQ) in schools. This study aims to assess the state of perceived IAQ in Slovenian school classrooms and its association with the selected IAQ factors to improve the understanding of perceived IAQ for designing public health interventions aimed to improve IAQ in schools. A national cross-sectional study was performed in all 454 Slovenian primary schools in the school year 2019/2020. The questionnaires were filled out by the 3rd-grade teachers with the support of the caretakers. Teachers rated the IAQ in the classroom as the worst in winter. We found that the teachers’ perceived IAQ in the classroom is statistically significantly associated with the micro location of the school and some of the IAQ factors. Poor IAQ is associated with reduced manual airing of classrooms due to the thermal comfort of the occupants. Interventions should be aimed at improving occupants’ adaptive behaviors to increase the frequency of natural ventilation in classrooms.

Poor indoor air quality (IAQ) in schools is associated with impacts on pupils’ health and learning performance. We aimed to identify the factors that affect IAQ in primary schools. The following objectives were set: (a) to develop a questionnaire to assess the prevalence of factors in primary schools, (b) to conduct content validity of the questionnaire, and (c) to assess the prevalence of factors that affect the IAQ in Slovenian primary schools. Based on the systematic literature review, we developed a new questionnaire to identify factors that affect the IAQ in primary schools and conducted its validation. The questionnaires were sent to all 454 Slovenian primary schools; the response rate was 78.19%. The results show that the most important outdoor factors were the school’s micro location and the distance from potential sources of pollution, particularly traffic. Among the indoor factors, we did not detect a pronounced dominating factor. Our study shows that the spatial location of schools is key to addressing the problem of IAQ in schools.

Air pollution is a significant issue that affects almost all the countries in the world while predominating in South Asian Regional countries due to poverty, less attention, and less awareness towards the implementation and obeying of air quality guidelines in public. As a developing country, Sri Lanka stands at an optimum state of national air quality compared to other SARC because it is an island with a minor population compared to India, Pakistan, Bangladesh, etc. Maldives and Bhutan lie straightforwardly in owing mild air quality in SARC. However, SARC is far behind the world in maintaining optimistic air quality nationwide. Ambient air pollution-attributable deaths have become interim in past decades, a severe burden to the sustainable existence of SARC. A well-established systematic epidemiological, empirical studies and revisions regarding air pollution, strategic planning for mitigating air pollution, and frequent Spatio-temporal pollution monitoring nodes are necessary for Sri Lanka to achieve the sustainable goal. Other South Asian countries: India, Bangladesh, Pakistan, Afghanistan, Nepal, Bhutan, and Maldives, also should pay attention to minimizing outdoor air pollution nationwide for the betterment of future existence.

Road pricing is advocated as an effective travel demand management strategy to alleviate traffic congestion and improve environmental conditions. This paper analyzes the impacts of cordon pricing on the population’s daily activity pattern and their exposure to particulate matter by integrating activity-based models with air quality and exposure models in the case of New York City. To estimate changes in public exposure under cordon pricing scenarios, we take a sample of employees and study their mobility behavior during the day, which is mainly attributed to the location of the work and the time spent at work. The selection of employees and their exposure during the duration of their work is due to the unavailability of exact activity patterns for each individual. We show that the Central Business District (CBD) experiences a high concentration of PM2.5 emissions. Results indicate that implementing cordon pricing scenarios can reduce the population-weighted mean of exposure to PM2.5 emissions by 7% to 13% for our sample and, in particular, by 22% to 28% for those who work in the CBD. Furthermore, using an experimental model and assuming constant conditions, we point out the positive influence on indoor exposure for two locations inside and outside the CBD in response to cordon pricing. Considering the correlation between long-term exposure to fine particulate matter and the risks of developing cardiovascular disease and lung cancer, our findings suggest that improved public health conditions could be provided by implementing cordon pricing in the New York City CBD.

Gaseous pollutants at the ground level seriously threaten the urban air quality environment and public health. There are few estimates of gaseous pollutants that are spatially and temporally resolved and continuous across China. This study takes advantage of big data and artificial-intelligence technologies to generate seamless daily maps of three major ambient pollutant gases, i.e., NO2, SO2, and CO, across China from 2013 to 2020 at a uniform spatial resolution of 10 km. Cross-validation between our estimates and ground observations illustrated a high data quality on a daily basis for surface NO2, SO2, and CO concentrations, with mean coefficients of determination (root-mean-square errors) of 0.84 (7.99 µg m−3), 0.84 (10.7 µg m−3), and 0.80 (0.29 mg m−3), respectively. We found that the COVID-19 lockdown had sustained impacts on gaseous pollutants, where surface CO recovered to its normal level in China on around the 34th day after the Lunar New Year, while surface SO2 and NO2 rebounded more than 2 times slower due to more CO emissions from residents' increased indoor cooking and atmospheric oxidation capacity. Surface NO2, SO2, and CO reached their peak annual concentrations of 21.3 ± 8.8 µg m−3, 23.1 ± 13.3 µg m−3, and 1.01 ± 0.29 mg m−3 in 2013, then continuously declined over time by 12 %, 55 %, and 17 %, respectively, until 2020. The declining rates were more prominent from 2013 to 2017 due to the sharper reductions in anthropogenic emissions but have slowed down in recent years. Nevertheless, people still suffer from high-frequency risk exposure to surface NO2 in eastern China, while surface SO2 and CO have almost reached the World Health Organization (WHO) recommended short-term air quality guidelines (AQG) level since 2018, benefiting from the implemented stricter “ultra-low” emission standards. This reconstructed dataset of surface gaseous pollutants will benefit future (especially short-term) air pollution and environmental health-related studies.