Synthesis of satellite and ground data provide unique perspectives for discovering the air pollution patterns: A case study in Guangdong Province, China.

Existing studies have analyzed the spatio-temporal patterns of air pollutants by combining ground and satellite measurements, primarily for cross-validation purposes. However, the unique characteristics and discrepancies between satellite and ground measurements have rarely been leveraged to understand pollution patterns and identify air pollution sources. To our best knowledge, this study is the first to utilize these discrepancies to holistically analyze the spatial and temporal patterns and investigate local biomass-burning effects on the five typical air pollutants: particulate matter (PM2.5)/aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3). Guangdong (GD) province was selected as a case study due to its complex air pollution sources and patterns. Ground-based analysis from 2015 to 2023 shows significant decreases in PM2.5, CO, NO2, and SO2, and a significant increase in O3 in urban areas, indicating the efficacy of stringent air pollution control policies. However, satellite analysis shows significant downtrend only in AOD, while the trends of other pollutants are almost negligible, which are likely to be evidence of industrial migration. Both measurements exhibit regular seasonal patterns for all air pollutants. In-depth time-series comparisons between ground and satellite data reveal seasonal consistency for NO2 but noticeable discrepancies for both AOD and CO, which could be attributed to urban-rural differences and local versus transported pollution sources. Spatially, AOD and NO2 exhibits the most significant regional discrepancies, followed by SO2 and CO, with higher values observed over Pearl River Delta (PRD) compared to non-PRD regions. O3 is more evenly distributed, showing more pronounced seasonal variations than regional differences. The synergetic use of satellite and ground measurements collectively verifies the significant local biomass-burning effects on the five pollutants. These findings can aid in developing more targeted air pollution control policies.

Comprehensive analysis of classroom microclimate in context to health-related national and international indoor air quality standards.

Indoor air quality (IAQ) and indoor air pollution are critical issues impacting urban environments, significantly affecting the quality of life. Nowadays, poor IAQ is linked to respiratory and cardiovascular diseases, allergic reactions, and cognitive impairments, particularly in settings like classrooms. Thus, this study investigates the impact of indoor environmental quality on student health in a university classroom over a year, using various sensors to measure 19 environmental parameters, including temperature, relative humidity, CO2, CO, volatile organic compounds (VOCs), particulate matter (PM), and other pollutants. Thus, the aim of the study is to analyze the implications of the indoor microclimate for the health of individuals working in the classroom, as well as its implications for educational outcomes. The data revealed frequent exceedances of international standards for formaldehyde (HCHO), VOC, PM2.5, NO, and NO2. HCHO and VOCs levels, often originating from building materials and classroom activities, were notably high. PM2.5 levels exceeded both annual and daily standards, while NO and NO2 levels, possibly influenced by inadequate ventilation, also surpassed recommended limits. Even though there were numerous exceedances of current international standards, the indoor microclimate quality index (IMQI) score indicated a generally good indoor environment, remaining mostly between 0 and 50 for this indicator. Additionally, analyses indicate a high probability that some indicators will exceed the current standards, and their values are expected to trend upwards in the future. The study highlighted the need for better ventilation and pollutant control in classrooms to ensure a healthy learning environment. Frequent exceedances of pollutant standards can suggest a significant impact on student health and academic performance. Thus, the present study underscored the importance of continuous monitoring and proactive measures to maintain optimal indoor air quality.

Pollution exposure in the first 3 months post transplant is associated with lower baseline FEV1 and higher CLAD risk.

BACKGROUND: Exposure to air pollution post-lung transplant has been shown to decrease graft and patient survival. This study examines the impact of air pollution exposure in the first 3 months post-transplant on baseline (i.e., highest) forced expiratory volume in 1 second (FEV1) achieved and development of chronic lung allograft dysfunction (CLAD). METHODS: Double-lung transplant recipients (n = 82) were prospectively enrolled for comprehensive indoor and personal environmental monitoring at 6- and 12-week post transplant and followed for >4 years. Associations between clinical and exposure variables were investigated using an exposomics approach followed by analysis with a Cox proportional hazards model. Multivariable analyses were used to examine the impact of air pollution on baseline % predicted FEV1 (defined as the average of the 2 highest values post transplant) and risk of CLAD. RESULTS: Multivariable analysis revealed a significant inverse relationship between personal black carbon (BC) levels and baseline % FEV1. The multivariable model indicated that patients with higher-than-median exposure to BC (>350 ng/m3) attained a baseline % FEV1 that was 8.8% lower than those with lower-than-median BC exposure (p = 0.019). Cox proportional hazards model analysis revealed that patients with high personal BC exposure had a 2.4 times higher hazard risk for CLAD than patients with low BC exposure (p = 0.045). CONCLUSIONS: Higher personal BC levels during the first 3 months post-transplant decrease baseline FEV1 and double the risk of CLAD. Strategies to reduce BC exposure early following a lung transplant may help improve lung function and long-term outcomes.

Fit-for-purpose WWTP unmanned aerial systems: A game changer towards an integrated and sustainable management strategy.

In the ongoing Anthropocene era, air quality monitoring constitutes a primary axis of European and international policies for all sectors, including Waste Water Treatment Plants (WWTPs). Unmanned Aerial Systems (UASs) with proper sensing equipment provide an edge technology for air quality and odor monitoring. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry has been used in civil engineering, environmental (water) quality assessment and lately for industrial facilities monitoring. This study constitutes a systematic review of the late advances and limitations of germane equipment and implementations. Despite their unassailable flexibility and efficiency, the employment of the aforementioned technologies in WWTP remote monitoring is yet sparse, partial, and concerns only particular aspects. The main finding of the review was the lack of a tailored UAS for WWTP monitoring in the literature. Therefore, to fill in this gap, we propose a fit-for-purpose remote monitoring system consisting of a UAS with a platform that would integrate all the required sensors for air quality (i.e., emissions of H2S, NH3, NOx, SO2, CH4, CO, CO2, VOCs, and PM) and odor monitoring, multispectral and thermal cameras for photogrammetric structural health monitoring (SHM) and wastewater/effluent properties (e.g., color, temperature, etc.) of a WWTP. It constitutes a novel, supreme and integrated approach to improve the sustainable management of WWTPs. Specifically, the developments that a fit-for-purpose WWTP UAS would launch, are fostering the decision-making of managers, administrations, and policymakers, both in operational conditions and in case of failures, accidents or natural disasters. Furthermore, it would significantly reduce the operational expenditure of a WWTP, ensuring personnel and population health standards, and local area sustainability.

Fine particulate matter (PM2.5) exposure assessment among active daily commuters to induce behaviour change to reduce air pollution.

Fine particulate matter (PM2.5), a detrimental urban air pollutant primarily emitted by traffic and biomass burning, poses disproportionately significant health risks at relatively limited exposure during commuting. Previous studies have mainly focused on fixed locations when assessing PM2.5 exposure, while neglecting pedestrians and cyclists, who often experience higher pollution levels. In response, this research aimed to independently validate the effectiveness of bicycle-mounted low-cost sensors (LCS) adopted by citizens, evaluate temporal and spatial PM2.5 exposure, and assess associated health risks in Ljubljana, Slovenia. The LCS quality assurance results, verified by co-location field tests by air quality monitoring stations (AQMS), showed comparable outcomes with an average percentage difference of 21.29 %, attributed to humidity-induced nucleation effects. The colder months exhibited the highest air pollution levels (µ = 32.31 µg/m3) due to frequent thermal inversions and weak wind circulation, hindering vertical air mixing and the adequate dispersion of pollutants. Additionally, PM2.5 levels in all sampling periods were lowest in the afternoon (µ = 12.09 µg/m3) and highest during the night (µ = 61.00 µg/m3) when the planetary boundary layer thins, leading to the trapping of pollutants near the surface, thus significantly affecting diurnal and seasonal patterns. Analysis of exposure factors revealed that cyclists were approximately three times more exposed than pedestrians. However, the toxicological risk assessment indicated a minimal potential risk of PM2.5 exposure. The collaborative integration of data from official AQMS and LCS can enhance evidence-based policy-making processes and facilitates the realignment of effective regulatory frameworks to reduce urban air pollution.

Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy.

Greenhouse gases absorb the Earth's thermal radiation and partially return it to the Earth's surface. When accumulated in the atmosphere, greenhouse gases lead to an increase in the average global air temperature and, as a result, climate change. In this paper, an approach to measuring CO2 and CH4 concentrations using Fourier transform infrared spectroscopy (FTIR) is proposed. An FTIR spectrometer mockup, operating in the wavelength range from 1.0 to 1.7 µm with a spectral resolution of 10 cm-1, is described. The results of CO2 and CH4 observations throughout a day in urban conditions are presented. A low-resolution FTIR spectrometer for the 16U CubeSat spacecraft is described. The FTIR spectrometer has a 2.0-2.4 µm spectral range for CO2 and CH4 bands, a 0.75-0.80 µm range for reference O2 bands, an input field of view of 10-2 rad and a spectral resolution of 2 cm-1. The capabilities of the 16U CubeSat spacecraft for remote sensing of greenhouse gas emissions using a developed FTIR spectrometer are discussed. The design of a 16U CubeSat spacecraft equipped with a compact, low-resolution FTIR spectrometer is presented.

Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring.

Public air quality monitoring relies on expensive monitoring stations which are highly reliable and accurate but require significant maintenance and cannot be used to form a high spatial resolution measurement grid. Recent technological advances have enabled air quality monitoring that uses low-cost sensors. Being inexpensive and mobile, with wireless transfer support, such devices represent a very promising solution for hybrid sensor networks comprising public monitoring stations supported by many low-cost devices for complementary measurements. However, low-cost sensors can be influenced by weather and degradation, and considering that a spatially dense network would include them in large numbers, logistically adept solutions for low-cost device calibration are essential. In this paper, we investigate the possibility of a data-driven machine learning calibration propagation in a hybrid sensor network consisting of One public monitoring station and ten low-cost devices equipped with NO2, PM10, relative humidity, and temperature sensors. Our proposed solution relies on calibration propagation through a network of low-cost devices where a calibrated low-cost device is used to calibrate an uncalibrated device. This method has shown an improvement of up to 0.35/0.14 for the Pearson correlation coefficient and a reduction of 6.82 µg/m3/20.56 µg/m3 for the RMSE, for NO2 and PM10, respectively, showing promise for efficient and inexpensive hybrid sensor air quality monitoring deployments.

Ambient air pollution and the risk of neurological diseases in residential areas near multi-purposed industrial complexes of korea: A population-based cohort study.

Emerging evidence suggest that long-term exposure to air pollution may induce adverse effects on the central nervous system. However, no study explored the associations in large industrial complex (IC) areas which are one of the major contributors to air pollution. Therefore, we aimed to investigate the pollution status and the association between residential proximity and incidence of neurological diseases near two major ICs characterized as multi-purposed ICs in Korea. A retrospective cohort of residents near the ICs was constructed using Korea's health insurance data and monitored from 2008 to 2019. Emission amounts of the ICs and the air pollution status in the nearby (exposed) and remote (control) area were evaluated using data from national regulatory networks, and hazard ratios (HRs) and 95% confidence intervals (CIs) for neurological diseases of the exposed group compared to the control group were calculated using Cox proportional regression models. Overall, the complexes emitted large amounts of VOCs, CO, NOx, and PM10, and annual levels of ambient PM (2.5, 10), gaseous substances (NO2, SO2), VOCs and PAHs were higher in the exposed area compared to the control and/or the national average. The risk of inflammatory disease of the CNS (G00-09) and extrapyramidal and movement disorders (G20-26) were higher in the exposed area with a HR (95% CI) of 1.36 (1.10-1.68) and 1.33 (1.27-1.39) respectively. Among the subclasses, other extrapyramidal and movement disorders (G25) and epilepsy (G40) were associated with higher risks in the exposed area (HR (95%CI): 1.11 (1.04-1.18), 1.08 (1.00-1.16)) after adjusting for potential confounders. These results suggest that people living near ICs are more likely to be exposed to higher air pollution levels and have higher risks of developing several neurological disorders. However, further epidemiological studies in these industrial areas supplemented with other indicators of environmental exposure and control of other diverse factors are warranted.

Direct quantification of PAHs and nitro-PAHs in atmospheric PM by thermal desorption gas chromatography with electron ionization mass spectroscopic detection.

In this work, we developed and optimized a method for the analysis of PAHs and nitro-PAHs in atmospheric particulate matter (PM) samples by using thermal desorption gas chromatography coupled with electron ionization single quadrupole mass spectrometry (TD-GC-(EI)-MS). The method uses thermal desorption from a PM on a filter sample as means of sample introduction to a column and obviates the need for complex extraction procedures, which are time-consuming and require environmentally unfriendly solvents. Moreover, the possibility of systematic errors is minimized and a significantly smaller amount of sample is required compared to traditional techniques requiring a pre-extraction step (approx. 10-times). Thirteen PAHs and three nitro-PAHs were used during method development. Although Tenax cartridges are typically used to capture volatile pollutants from the air, we found that glass-wool liner is the most suitable trap for the examined analytes after desorption from a quartz filter. Among the various instrument parameters which were tested and optimized, TD desorption flow and hold time, and temperature of the cooled injection system (CIS) proved to be most critical. We also found out that the matrix effect is especially pronounced in the case of high PM loadings, which should be kept in mind when planning the analysis. After the optimization, standard reference materials (ERM-CZ100 and NIST 1648a) were used for partial method validation and finally, real PM10 and PM2.5 samples from two Slovenian cities were successfully analyzed.

A Focus on Electromobility within Smart City Solutions-Charging Stations, Renewable Energy, and Air Quality Monitoring.

This paper reviews some of the existing methods for charging electric vehicles, generating renewable energy, and storing it. Plans of practical implementation in the city of Brno are compared with the situation in Glasgow. Moreover, it is essential to pay attention to integrated solutions in order to increase efficiency. Energy harvesting and charging systems are combined with an air quality measurement system and integrated into LED street lights. The collected data are sent to a central server for evaluation. The use of smart solutions is a modern approach to saving energy and reducing CO2 emissions in many sectors. As an example, the described solutions can be applied dually, in both civilian and military sectors. Considering the potential benefits of easier logistics or quiet operation, the potential military exploitation of technological capabilities is discussed from the perspective of enhancing citizens' security and safety in cities.

Challenges and Opportunities for Printed Electrical Gas Sensors.

Printed electrical gas sensors are a low-cost, lightweight, low-power, and potentially disposable alternative to gas sensors manufactured using conventional methods such as photolithography, etching, and chemical vapor deposition. The growing interest in Internet-of-Things, smart homes, wearable devices, and point-of-need sensors has been the main driver fueling the development of new classes of printed electrical gas sensors. In this Perspective, we provide an insight into the current research related to printed electrical gas sensors including materials, methods of fabrication, and applications in monitoring food quality, air quality, diagnosis of diseases, and detection of hazardous gases. We further describe the challenges and future opportunities for this emerging technology.

Optimizing Urban Air Pollution Detection Systems.

Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.

Variations in traffic-related polycyclic aromatic hydrocarbons in PM2.5 in Kanazawa, Japan, after the implementation of a new vehicle emission regulation.

A three-year sampling campaign was conducted at a roadside air pollution monitoring station in the urban area of Kanazawa, Japan. Due to a new emission regulation, PAHs levels decreased over the sampling campaign, exhibiting values of 706 ± 413 pg/m3 in 2017, 559 ± 384 pg/m3 in 2018, and 473 ± 234 pg/m3 in 2019. In each year, similar seasonal variations in PAHs levels were observed, with higher levels observed in winter and lower levels in summer. Among the PAHs isomer ratios, we observed that the ratio of benzo[b]fluoranthene (BbF) and benzo[k]fluoranthene (BkF), [BbF]/([BbF] + [BkF]), and the ratio of indeno[1,2,3-cd]pyrene (IDP) and benzo[ghi]perylene (BgPe), [IDP]/([BgPe] + [IDP]), showed stability over the sampling campaign and were less affected by the new emission regulation, seasonal variations, and regional characteristics. When using the combined ratio ranges of 0.66 - 0.80 ([BbF]/([BbF] + [BkF]) and 0.26-0.49 ([IDP]/([BgPe] + [IDP]), traffic emissions were clearly distinguished from other PAHs emission sources. Principal component analysis (PCA) and positive matrix factorization (PMF) were also performed to further analyse the characteristics of traffic-related PAHs. Overall, this study affirmed the effectiveness of the new emission regulation in the reduction of PAHs emissions and provided a combined range for identifying PAHs traffic emission sources.

Sampling Low Air Pollution Concentrations at a Neighborhood Scale in a Desert U.S. Metropolis with Volatile Weather Patterns.

BACKGROUND: Neighborhood-scale air pollution sampling methods have been used in a range of settings but not in low air pollution airsheds with extreme weather events such as volatile precipitation patterns and extreme summer heat and aridity-all of which will become increasingly common with climate change. The desert U.S. metropolis of Tucson, AZ, has historically low air pollution and a climate marked by volatile weather, presenting a unique opportunity. METHODS: We adapted neighborhood-scale air pollution sampling methods to measure ambient NO2, NOx, and PM2.5 and PM10 in Tucson, AZ. RESULTS: The air pollution concentrations in this location were well below regulatory guidelines and those of other locations using the same methods. While NO2 and NOx were reliably measured, PM2.5 measurements were moderately correlated with those from a collocated reference monitor (r = 0.41, p = 0.13), potentially because of a combination of differences in inlet heights, oversampling of acutely high PM2.5 events, and/or pump operation beyond temperature specifications. CONCLUSION: As the climate changes, sampling methods should be reevaluated for accuracy and precision, especially those that do not operate continuously. This is even more critical for low-pollution airsheds, as studies on low air pollution concentrations will help determine how such ambient exposures relate to health outcomes.

Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County.

Non-governmental air quality monitoring networks include low-cost, networked air pollution sensors hosted at homes and schools that display real-time pollutant concentration estimates on publicly accessible websites. Such networks can empower people to take health-protective actions, but their unplanned organization may produce an uneven spatial distribution of sensors. Barriers to acquiring sensors may disenfranchise particular social groups. To test this directly, we quantitatively examine if there are social inequalities in the distribution of sensors in a non-governmental air quality monitoring network (PurpleAir) in Los Angeles County, California. We paired sociodemographic data from the American Community Survey and estimates of PM2.5 concentrations from the USEPA's Downscaler model at the census tract level (n = 2203) with a sensors per capita (SPC) variable, which is based on population proximity to PurpleAir sensors (n = 696) in Los Angeles County. Findings from multivariable generalized estimating equations (GEEs) controlling for clustering by housing age and value reveal patterns of environmental injustice in the distribution of PurpleAir sensors across Los Angeles County census tracts. Tracts with higher percentages of Hispanic/Latino/a and Black residents and lower median household income had decreased SPC. There was a curvilinear (concave) relationship between the percentage of renter-occupants and SPC. Sensors were concentrated in tracts with greater percentages of adults and seniors (vs. children), higher occupied housing density, and higher PM2.5 pollution. Results reveal social inequalities in the self-organizing PurpleAir network, suggesting another layer of environmental injustice such that residents of low-income and minority neighborhoods have reduced access to information about local air pollution.

Fast Quantification of Air Pollutants by Mid-Infrared Hyperspectral Imaging and Principal Component Analysis.

An imaging Fourier-transform spectrometer in the mid-infrared (1850-6667 cm-1) has been used to acquire transmittance spectra at a resolution of 1 cm-1 of three atmospheric pollutants with known column densities (Q): methane (258 ppm·m), nitrous oxide (107.5 ppm·m) and propane (215 ppm·m). Values of Q and T have been retrieved by fitting them with theoretical spectra generated with parameters from the HITRAN database, based on a radiometric model that takes into account gas absorption and emission, and the instrument lineshape function. A principal component analysis (PCA) of experimental data has found that two principal components are enough to reconstruct gas spectra with high fidelity. PCA-processed spectra have better signal-to-noise ratio without loss of spatial resolution, improving the uniformity of retrieval. PCA has been used also to speed up retrieval, by pre-calculating simulated spectra for a range of expected Q and T values, applying PCA to them and then comparing the principal components of experimental spectra with those of the simulated ones to find the gas Q and T values. A reduction in calculation time by a factor larger than one thousand is achieved with improved accuracy. Retrieval can be further simplified by obtaining T and Q as quadratic functions of the two first principal components.

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa.

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), and nitrogen dioxide (NO2) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively). Furthermore, higher levels of NO2 and SO2 were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO2 models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO2 models were less robust with lower R2 annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R2 for PM10 models ranged from 52% to 80% while for PM2.5 models this range was 61-76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO2 concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.

Collision-Free Transmissions in an IoT Monitoring Application Based on LoRaWAN.

With the Internet of Things (IoT), the number of monitoring applications deployed is considerably increasing, whatever the field considered: smart city, smart agriculture, environment monitoring, air pollution monitoring, to name a few. The LoRaWAN (Long Range Wide Area Network)architecture with its long range communication, its robustness to interference and its reduced energy consumption is an excellent candidate to support such applications. However, if the number of end devices is high, the reliability of LoRaWAN, measured by the Packet Delivery Ratio (PDR), becomes unacceptable due to an excessive number of collisions. In this paper, we propose two different families of solutions ensuring collision-free transmissions. The first family is TDMA (Time-Division Multiple Access)-based. All clusters transmit in sequence and up to six end devices with different spreading factors belonging to the same cluster are allowed to transmit in parallel. The second family is FDMA (Frequency Divsion Multiple Access)-based. All clusters transmit in parallel, each cluster on its own frequency. Within each cluster, all end devices transmit in sequence. Their performance are compared in terms of PDR, energy consumption by end device and maximum number of end devices supported. Simulation results corroborate the theoretical results and show the high efficiency of the solutions proposed.

Urban air pollution monitoring using scanning Lidar.

We have developed a scanning Lidar system in this work to detect urban air pollution changes in real time and locate the sources of urban air pollution. We first proposed an algorithm to retrieve atmospheric extinction coefficients, which we used to create Lidar maps. Using Lidar map of the average extinction coefficients, we identified the locations of the local maximum values, and hence, the positions of the urban air pollution sources. Experimental results indicate that this method is effective for urban air pollution monitoring.

The Use of the Internet of Things for Estimating Personal Pollution Exposure.

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases.