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.

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.

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.

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.

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.

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.

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.

Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities-a case study in Sheffield.

Traditional real-time air quality monitoring instruments are expensive to install and maintain; therefore, such existing air quality monitoring networks are sparsely deployed and lack the measurement density to develop high-resolution spatiotemporal air pollutant maps. More recently, low-cost sensors have been used to collect high-resolution spatial and temporal air pollution data in real-time. In this paper, for the first time, Envirowatch E-MOTEs are employed for air quality monitoring as a case study in Sheffield. Ten E-MOTEs were deployed for a year (October 2016 to September 2017) monitoring several air pollutants (NO, NO2, CO) and meteorological parameters. Their performance was compared to each other and to a reference instrument installed nearby. E-MOTEs were able to successfully capture the temporal variability such as diurnal, weekly and annual cycles in air pollutant concentrations and demonstrated significant similarity with reference instruments. NO2 concentrations showed very strong positive correlation between various sensors. Mostly, correlation coefficients (r values) were greater than 0.92. CO from different sensors also had r values mostly greater than 0.92; however, NO showed r value less than 0.5. Furthermore, several multiple linear regression models (MLRM) and generalised additive models (GAM) were developed to calibrate the E-MOTE data and reproduce NO and NO2 concentrations measured by the reference instruments. GAMs demonstrated significantly better performance than linear models by capturing the non-linear association between the response and explanatory variables. The best GAM developed for reproducing NO2 concentrations returned values of 0.95, 3.91, 0.81, 0.005 and 0.61 for factor of two (FAC2), root mean square error (RMSE), coefficient of determination (R2), normalised mean biased (NMB) and coefficient of efficiency (COE), respectively. The low-cost sensors offer a more affordable alternative for providing real-time high-resolution spatiotemporal air quality and meteorological parameter data with acceptable performance.

Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development.

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75⁻80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system's effectiveness.

Atmospheric Pollution Monitoring in Urban Area by Employing a 450-nm Lidar System.

In past decades, lidar techniques have become main tools for atmospheric remote sensing. However, traditional pulsed lidar systems are relatively expensive and require considerable maintenance. These shortcomings may be overcome by the development of a blue band Scheimpflug lidar system in Dalian, Northern China. Atmospheric remote measurements were carried out for 10 days in an urban area to validate the feasibility and performance of a 450-nm Scheimpflug lidar system. A 24-h continuous measurement was achieved in winter on a near horizontal path with an elevation angle of about 6.4°. The aerosol extinction coefficient retrieved by the Fernald-inversion algorithm shows good agreement with the variation of PM10/PM2.5 concentrations recorded by a national pollution monitoring station. The experimental result reveals that the linear ratio between the aerosol extinction coefficient and the PM10 concentration under high relative humidity (75⁻90%) is about two-times that in low relative humidity (≤75%) when the PM10 concentrations are less than 100 µg/m³.

Support Vector Machine Regression for Calibration Transfer between Electronic Noses Dedicated to Air Pollution Monitoring.

Recently, the emergence of low-cost sensors have allowed electronic noses to be considered for densifying the actual air pollution monitoring networks in urban areas. Electronic noses are affected by changes in environmental conditions and sensor drifts over time. Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different. For these reasons, the calibration process has become very expensive and time consuming. To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative. Among them, direct standardization shows good efficiency for calibration transfer. In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint x-y distances) for data selection and support vector machine regression to match between electronic noses. The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide. Experimental results show that our method gave the highest efficiency compared to classical direct standardization.

Towards an integrated framework for air quality monitoring and exposure estimation-a review.

For the health and safety of the public, it is essential to measure spatiotemporal distribution of air pollution in a region and thus monitor air quality in a fine-grain manner. While most of the sensing-based commercial applications available until today have been using fixed environmental sensors, the use of personal devices such as smartphones, smartwatches, and other wearable devices has not been explored in depth. These kinds of devices have an advantage of being with the user continuously, thus providing an ability to generate accurate and well-distributed spatiotemporal air pollution data. In this paper, we review the studies (especially in the last decade) done by various researchers using different kinds of environmental sensors highlighting related techniques and issues. We also present important studies of measuring impact and emission of air pollution on human beings and also discuss models using which air pollution inhalation can be associated to humans by quantifying personal exposure with the use of human activity detection. The overarching aim of this review is to provide novel and key ideas that have the potential to drive pervasive and individual centric and yet accurate pollution monitoring techniques which can scale up to the future needs.

A Modular Plug-And-Play Sensor System for Urban Air Pollution Monitoring: Design, Implementation and Evaluation.

Urban air pollution has caused public concern globally because it seriously affects human life. Modern monitoring systems providing pollution information with high spatio-temporal resolution have been developed to identify personal exposures. However, these systems' hardware specifications and configurations are usually fixed according to the applications. They can be inconvenient to maintain, and difficult to reconfigure and expand with respect to sensing capabilities. This paper aims at tackling these issues by adopting the proposed Modular Sensor System (MSS) architecture and Universal Sensor Interface (USI), and modular design in a sensor node. A compact MSS sensor node is implemented and evaluated. It has expandable sensor modules with plug-and-play feature and supports multiple Wireless Sensor Networks (WSNs). Evaluation results show that MSS sensor nodes can easily fit in different scenarios, adapt to reconfigurations dynamically, and detect low concentration air pollution with high energy efficiency and good data accuracy. We anticipate that the efforts on system maintenance, adaptation, and evolution can be significantly reduced when deploying the system in the field.

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks.

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems.

The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of micro-level pollution information, including human personal exposure and acute exposure to air pollutants. A real-time system with high spatio-temporal resolution is essential because of the limited data availability and non-scalability of conventional air pollution monitoring systems. Currently, researchers focus on the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) and have achieved significant breakthroughs by utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN). However, there exist potential problems of these newly proposed systems, namely the lack of 3D data acquisition ability and the flexibility of the sensor network. In this paper, we classify the existing works into three categories as Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) based on the carriers of the sensors. Comprehensive reviews and comparisons among these three types of sensor networks were also performed. Last but not least, we discuss the limitations of the existing works and conclude the objectives that we want to achieve in future systems.

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.

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.