Traditional Air Quality Research Articles

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

Spatial Variation of Pollutant (NO2, SO2 & CO) and Its Impact Factors in Jakarta: An Application of Sentinel-5P Products

Abstract This study investigates the impact of transportation, manufacturing, and energy sectors on air quality in Jakarta using Geographically Weighted Regression (GWR) and remote sensing data. Utilizing datasets from Sentinel-5P for NO2, SO2, and CO concentrations, and predictor variables including road density, industrial activity, and energy production, the research aims to quantify the spatial heterogeneity of air pollution. The findings reveal significant spatial variations in pollutant concentrations across Jakarta and its neighbouring provinces, Banten and West Java, during the dry and wet months of 2023. High NO2 levels were primarily linked to dense traffic in central Jakarta, while elevated SO2 and CO concentrations were associated with industrial and energy activities in the peripheral regions. Seasonal analysis indicated that meteorological factors play a crucial role in pollutant dispersion. The integration of remote sensing data with GWR provided a robust framework for environmental monitoring, addressing gaps in traditional air quality observation methods. This research contributes to the broader understanding of urban air pollution dynamics and offers a replicable model for other regions facing similar challenges.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

A multi-task stations cooperative air quality prediction system for sustainable development

In recent years, A series of environmental problems caused by air pollution have attracted widespread attention. Air quality forecasting has become an indispensable part of people’s daily life. However, the traditional air quality prediction (AQP) model WRF-CMAQ simulation system faces several challenges: (1) the fuzziness of pollutant formation mechanism; (2) the hardness of integrating the features of meteorological conditions; (3) the difficulty of cooperating among monitoring stations. To deal with these challenges, we propose a multi-task station cooperative air quality prediction (MTSC-AQP) system for sustainable development. The MTSC-AQP system contains three modules: air quality analysis, a WRF-LSTM module for initial pollutant prediction, and multi-station cooperative AQP. Through air quality analysis, it can calculate the air quality index (AQI) and analyze the correlation between pollutants and meteorological conditions. Then, the WRF-LSTM module integrates spatio-temporal multi-source data to forecast the initial concentration of pollutants. Finally, the system incorporates the gravity model to predict the final AQI. Extensive experiments conducted on real-world datasets show the effectiveness of forecasting the AQI by using the MTSC-AQP system.

Predicting PM2.5 concentrations in Ba Ria - Vung Tau province using an artificial neural network model

Abstract Air pollution has increased rapidly and significantly recently, particularly in big cities. Various methods for predicting air pollution are available, including traditional air quality models, statistical techniques, and artificial intelligence. In this study, the author developed a model using a Feed Forward Neural Network with multivariate statistical methods to predict air pollution. Data from three automated air monitoring locations in Ba Ria-Vung Tau province were gathered between 2020 and 2022 to forecast the concentration of PM2.5. The results demonstrated that the FFNN model with an I(6)-HL(5)-O(1) structure outperformed other models in predicting PM2.5 concentration. The training, validation, and testing phases yielded mean squared error values of 9.2×10−6, 8.2×10−6, and 8.6×10−6, respectively. The regression coefficient obtained consistently high values across a range of experiments (above 0.99). The MSE value of the FFNN model of the prediction set was lower than that of the NSE value, which was higher than those obtained from the multiple linear regression.

An improved cohesive hierarchical clustering for indoor air quality monitoring in smart gymnasium with healthy sport areas

With the rise of healthy lifestyles, sports arenas have become important exercise venues for people. However, indoor air quality issues are becoming increasingly prominent, posing a potential threat to the health of athletes. Traditional indoor air quality monitoring systems often suffer from problems such as unreasonable layout of monitoring points and inaccurate data processing, making it difficult to effectively cope with complex indoor environments. This article proposes an intelligent monitoring system for indoor air quality in sports venues based on an improved cohesive hierarchical clustering algorithm to address these issues. The system optimizes the layout of monitoring points, combines real-time data collection on the Android platform, and accurately processes non smooth data using an improved cohesive hierarchical clustering algorithm based on BiLSTM. The experimental results show that the algorithm not only improves the accuracy of monitoring point selection, but also optimizes the number of monitoring points and effectively supplements missing data. In addition, the algorithm significantly reduces computational complexity and improves computational efficiency while ensuring clustering quality. Application testing shows that the indoor air environment monitoring system constructed in this article can obtain and analyze data in real time, effectively control the concentration of key pollutants such as PM2.5 and carbon dioxide, and create a healthier sports environment for sports venues. This study provides new ideas and methods for indoor air quality monitoring, which has important practical application value.

Smart environmental drone utilization for monitoring urban air quality

Urban air quality has significant and far-reaching impacts on both human health and the broader environment. Pollutants like particulate matter (PM2.5 and PM10), for instance, are associated with a range of health issues including respiratory conditions, asthma, heart diseases, and even contribute to low birth weight in newborns. These health implications extend to larger environmental concerns such as air pollution, greenhouse gas emissions, and global climate change. Recognizing the urgent need for effective and dynamic air quality monitoring solutions, this paper explores the use of smart environmental drones as a promising approach. Our drone is equipped with a state-of-the-art, low-cost particulate matter sensor that can accurately measure PM2.5 and PM10 concentrations. Operating at a flight speed of 10 m/s and capable of covering a range of 5 km, the drone executes a pre-programmed flight plan to autonomously map pollution levels across urban areas. With a 95% accuracy rate in sensor readings, our model significantly minimizes potential errors commonly associated with traditional air quality monitoring methods. Furthermore, it simplifies maintenance procedures, reducing both time and financial costs. By employing drone technology in this innovative manner, our model offers a cost-effective, reliable, and dynamic solution for monitoring urban air quality. It provides real-time, actionable pollution indices that can inform public health decisions, regulatory policies, and community awareness, thereby contributing to the broader goal of improving air quality and public health.

Integrating Passive Biomonitoring and Active Monitoring: Spider Web Silk and Portable Instruments for Air Quality in Urban Areas

Urban areas worldwide face significant challenges from increasing air pollution, posing health risks and environmental concerns. Lahore, a major city in Pakistan, is particularly affected by severe air pollution due to rapid industrial growth, high vehicle emissions, and various human activities. Traditional air quality monitoring methods, while effective, are often costly and complex, limiting their widespread use. This study investigates a dual monitoring system combining passive biomonitoring with spider web silk and active measurements using portable instruments to assess airborne pollutants in urban environments. Spider webs, collected from various sites in Lahore, were analyzed for heavy metals, while portable instruments simultaneously measured PM2.5, PM10, total volatile organic contents, formaldehyde, and carbon monoxide at the same locations. A spatial distribution analysis using GIS and statistical analysis revealed a pattern with significant changes as per land use in the urban environment related to anthropogenic activities. The Principal Component Analysis revealed three distinct clusters of pollutants origins consisting of: I) effluent drains and landfills, II) construction sites, and III) residential areas. The concentrations of Hg in spider silk (average ~ 2.66 mg/kg) were found to be significantly higher in the northeastern part of Lahore, with a similar trend observed in Cd, Cu, Ni, and As levels (average ~ 15.45, 102.87, 31.72, and 6.64, respectively). Several-fold changes in levels and spatial distribution of Pb (15.12 to 356.41 mg/kg) showed a higher concentration in the northern and northeastern parts of Lahore. The spatial variation pattern of Cr (average~ 57.04 mg/kg) registered a higher concentration in southwestern Lahore. Ambient air levels of measured pollutants followed almost similar patterns in spatial distribution. PM2.5 and PM10 measured higher levels (195 and 226 μg/m3, respectively) in the southwestern and northwestern areas, with a similar pattern of variation observed in TVOC (average ~ 0.14 μg/m3). The concentration of HCHO (average ~ 0.003 μg/m3) was higher in the southwest of Lahore. All measured pollutants registered higher values than air quality standards. Particulate matter is the most dominant pollutant contributing to air pollution (up to 20x higher than WHO guidelines). The findings support the use of a dual monitoring system, integrating passive spider web silk biomonitoring and active portable instruments, as a scalable and sustainable solution for air quality management. This approach holds potential for global application in diverse urban environments, with future research focusing on further validation and integration with advanced remote sensing technologies to enhance air quality monitoring and contribute to improved public health and environmental management worldwide. Hence, this research work points to the potential use of spider silk as a biomonitoring tool in combination with instrumental measurement of ambient air pollutants.

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.

Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting.

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios.

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.

Identifying risk information needs of rural communities impacted by wildfire smoke: A mental models approach

As wildfire activity continues to increase throughout much of the western US, questions remain about how to effectively communicate wildfire smoke risk to harder-to-reach groups, including rural populations. Standard public health strategies rely largely on risk assessment and communication informed by available air quality data. However, rural populations can be difficult to reach through traditional communication channels and air quality monitoring to inform messaging can be absent or scarce. As efforts increase to expand air quality monitoring into rural regions, there is a need to identify and affirm the most effective content and communication channels for motivating protective action. We used a mental models approach to identify wildfire smoke risk information needs and preferred communication channels in three rural counties in northern Nevada. Participants revealed a substantial knowledge base with opportunities for enhancement related to the range of potential physical and mental health impacts, vulnerable groups, and exposure mitigation strategies. Preferred communication channels were varied but almost exclusively local. The mental models approach also uncovered important barriers to exposure mitigation as well as potential areas of future research. Insights gained from this study will be used to inform targeted wildfire smoke risk communication for rural Nevada counties and may serve to motivate similar studies in other rural regions. Identifying similarities and differences in information needs and preferred communication channels can help inform understanding of how and why to tailor wildfire smoke risk communication. Any new messaging developed from such studies should be evaluated to ensure its effectiveness.

A two-way coupled regional urban–street network air quality model system for Beijing, China

Abstract. Owing to the substantial traffic emissions in urban areas, especially near road areas, the concentrations of pollutants, such as ozone (O3) and its precursors, have a large difference compared to regional averages, and their distributions cannot be captured accurately by traditional single-scale air quality models. In this study, a new version of a regional urban–street network model (an Integrated Air Quality Modeling System coupling regional urban–street: IAQMS-street v2.0) is presented. An upscaling module is implemented in IAQMS-street v2.0 to calculate the impact of mass transfer to regional scale from street network. The influence of pollutants in the street network is considered in the concentration calculation on the regional scale, which is not considered in a previous version (IAQMS-street v1.0). In this study, the simulated results in Beijing during August 2021, using IAQMS-street v2.0, IAQMS-street v1.0, and the regional model (Nested Air Quality Prediction Modeling System, NAQPMS), are compared. On-road traffic emissions in Beijing, as the key model input data, were established using intelligent image-recognition technology and real-time traffic big data from navigation applications. The simulated results showed that the O3 and nitrogen oxide (NOx) concentrations in Beijing were reproduced by using IAQMS-street v2.0 on both the regional scale and street scale. The prediction fractions within a factor of 2 (FAC2s) between simulations and observations of NO and NO2 increased from 0.11 and 0.34 in NAQPMS to 0.78 and 1.00 in IAQMS-street v2.0, respectively. The normalized mean biases (NMBs) of NO and NO2 decreased from 2.67 and 1.33 to −0.25 and 0.08. In the coupled model, the concentration of NOx at the street scale is higher than that at the regional scale, and the simulated distribution of pollutants on a regional scale was improved in IAQMS-street v2.0 when compared with that in IAQMS-street v1.0. We further used IAQMS-street v2.0 to quantify the contribution of local on-road traffic emissions to the O3 and NOx emissions and analyze the effect of traffic regulation policies in Beijing. Results showed that heavy-duty trucks are the major source of on-road traffic emissions of NOx. The relative contributions of local traffic emissions to NO2, NO, and O3 concentrations were 53.41 %, 57.45 %, and 8.49 %, respectively. We found that traffic regulation policies in Beijing largely decreased the concentrations of NOx and hydrocarbons (HC); however, the O3 concentration near the road increased due to the decrease consumption of O3 by NO. To decrease the O3 concentration in urban areas, controlling the local emissions of HC and NOx from other sources requires consideration.

Air Quality Monitoring and Analysis for Sustainable Development of Solid Waste Dump Yards Using Smart Drones and Geospatial Technology

Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH4 recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 μg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

System for performance evaluation and calibration of low-cost gas sensors applied to air quality monitoring

The increasing emissions of air pollutants have been a current issue and with great interest by regulatory agencies, industries, and society in general as they represent a threat to health and to the environment. The monitoring using low-cost atmospheric sensors has been an emerging alternative with great prospect of use to complement the data obtained by traditional air quality monitoring stations. However, the data obtained by these low-cost sensors are still questionable and can often be unreliable. The objective of this work was to build a system for calibration and evaluation of performance parameters of low-cost monitor comparing their results to reference analyzers. In this sense, three low-cost sensors for CO, O3 and SO2 of a monitor were calibrated and evaluated against the performance parameters defined by baseline drift, linearity, precision, bias, accuracy, response time, interference of pollutant gases and long-term drift. There was no significant deviation from baseline drift for any sensor. All sensors showed a linear profile (R2ADJ ∼1), good precision (>95%), and a response time of less than 2 min. After calibration, satisfactory accuracy (>89%) and bias (close to zero) data were obtained. The SO2 sensor showed signal disturbances in the presence of different concentrations of CO. Long-term drift suggests a calibration periodicity of less than 60 days. To use these sensors to monitor air quality, prior calibration is necessary, and the determination of performance parameters is suggested.

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building

The measurement of the CO2 concentration has a wide range of applications. Traditionally, it has been used to assess air quality, with other applications linked to the experimental assessment of occupancy patterns and air renewal rates. More recently, the worldwide dissemination of COVID-19 establishing a relationship between infection risk and the mean CO2 level has abruptly led to the measurement of the CO2 concentration in order to limit the spread of this respiratory disease in the indoor environment. Therefore, the extensive application of this measurement outside of traditional air quality assessment requires an in-depth analysis of the suitability of these sensors for such modern applications. This paper discusses the performance of an array of commercial wall-mounted CO2 sensors, focusing on their application to obtain occupancy patterns and air renovation rates. This study is supported by several long-term test campaigns conducted in an in-use office building located in south-eastern Spain. The results show a spread of 19-101 ppm, with a drift of 28 ppm over 5 years, an offset of 2-301 ppm and fluctuations up to 80 ppm in instantaneous measurements not related to concentration changes. It is proposed that values averaged over 30 min, using a suitable reference value, be used to avoid erroneous results when calibration is not feasible.

Analysis of the Temporal Distribution Characteristics of PM2.5 Concentration and Risk Evaluation of Its Inhalation Exposure.

PM2.5 poses a threat to human health. It is important to evaluate the potential risk of PM2.5 inhalation exposure when people are located in different spatiotemporal activity locations. In this study, the PM2.5 concentration was detected by the atmospheric cruise monitoring system (ACMS), a new detection technology used for city-wide PM2.5 concentration monitoring. People were divided into eight categories of five typical activity patterns, including rest (R), sedentary behavior (SB), light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA). The PM2.5 inhalation exposure risk was then estimated for these typical activities. The research results showed that the time sequence data of the ACMS had a similar tendency to change as those of the traditional air quality monitoring stations (AQMS). Although both passed the stationarity test, the relative error (RE) of the monthly average PM2.5 concentration between the ACMS and AQMS was 7.5-14%. RE was usually lower when the individual air quality index (IAQI) of PM2.5 was higher. Otherwise, RE was higher. The research results also showed that PM2.5 exposure was positively correlated with PM2.5 concentration, respiration rate, and human activity patterns. Because adults had a higher monthly average potential exposure (MAPE) than minors and that males had a higher MAPE than females. The potential exposure generated by LPA and MPA reached 50.76% of the total potential exposure (TPE). VPA brought about a 14.7% increase in the TPE. The research findings are helpful to understand the temporal distribution characteristics of PM2.5 concentrations and guide the potential risk evaluation of PM2.5 inhalation exposure.

Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ

Air pollution-induced issues involve public health, environmental, agricultural and socio-economic aspects. Therefore, decision-makers need low-cost, efficient tools with high spatiotemporal representation for monitoring air pollutants around urban areas and sensitive regions. Air pollution forecasting models with different time steps and forecast lengths are used as an alternative and support to traditional air quality monitoring stations (AQMS). In recent decades, given their eligibility to reconcile the relationship between parameters of complex systems, artificial neural networks have acquired the utmost importance in the field of air pollution forecasting. In this study, different machine learning regression methods are used to establish a mathematical relationship between air pollutants and meteorological factors from four AQMS (A-D) located between Çerkezköy and Süleymanpaşa, Tekirdağ. The model input variables included air pollutants and meteorological parameters. All developed models were used with the intent to provide instantaneous prediction of the air pollutant parameter NOx within the AQMS and across different stations. In the GMDH (group method of data handling)-type neural network method (namely the self-organizing deep learning approach), a five hidden layer structure consisting of a maximum of five neurons was preferred and, choice of layers and neurons were made in a way to minimize the error. In all models developed, the data were divided into a training (%80) and a testing set (%20). Based on R2, RMSE, and MAE values of all developed models, GMDH provided superior results regarding the NOx prediction within AQMS (reaching 0.94, 10.95, and 6.65, respectively for station A) and between different AQMS. The GMDH model yielded NOx prediction of station B by using station A input variables (without using NOx data as model input) with R2, RMSE and MAE values 0.80, 10.88, 7.31 respectively. The GMDH model is found suitable for being employed to fill in the gaps of air pollution records within and across-AQMS.