Monitoring Climate Change and Air Quality Project Initiative: A Case Study of the Collaboration between ECSTAR and TeroSpace in Thailand

In Thailand, the alteration of weather patterns has resulted in an increase in instances of irregular rainfall, contributing to the occurrence of droughts. The decline of water levels in dams, due to the combined effects of climate change and prolonged droughts, has had a significant impact on agricultural productivity. Drought has a profound effect on the terrestrial biosphere and the atmospheric water cycle and can also contribute to air pollution. Researchers have found a strong correlation between air pollution and drought severity. In response to this pressing issue, the Excellence Center of Space Technology and Research (ECSTAR) at King Mongkut’s Institute of Technology Ladkrabang has joined forces with TeroSpace company to launch an initiative aimed at promoting sustainable growth in Chiang Rai, a province in Thailand known for its rich biodiversity. ECSTAR and TeroSpace’s partnership on the sustainable growth initiative in Thailand’s Chiang Rai province focuses on expanding their collaboration to include international organizations such as the Centre National d’Etudes Spatiales (CNES), which will provide access to satellite imagery and climate and weather information to improve decision-making in various areas of development. CNES is a French organization in charge of space-related activities in France. The collaboration between France and Thailand for this project, in the context of the France-Thailand Year of Innovation 2023, will be crucial for the successful initiation and execution of this research project. The project aims to explore the relationship between air pollution and climate change through the deployment of air quality monitoring devices in designated locations, connected to a global data-sharing network. The results of this research will be valuable to policymakers as they consider the interplay between air pollution and climate change and make efforts to address these challenges.

Air quality nearby road traffic tunnel portals: BTEX monitoring

A monitoring campaign of BTEX （benzene, toluene, ethylbenzene, o- m- and p-xylene） was carried out nearby two tunnel portals in the urban area of Naples with the aim to verify air quality in this kind of urban sites. Sampling was carried out using the active adsorption technique. Sampling time was 1 h. Ambient temperature and traffic flow measurements were carried out during each sampling operation. The results indicate that average benzene concentrations at both sites exceed the limit value of 10 μg/Nm^3 established by the European Community （EC） （Dir. 2000/69）. Concentration levels of other BTEX are relatively high as well. A correlation between BTEX concentration and two wheeler vehicle flow was observed.

Real-time air pollution monitoring with sensors on city bus

This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles.The study is part of the GreenIoT project in Sweden,which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala.Through deploying low-cost wireless sensors,it is possible to obtain more fine-grained and real-time air pollution levels at different locations.The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala.The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed.Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.

Optimized two-level placement of test points for multi-objective air monitoring of the Three Gorges Reservoir area

To fit the complicated geographic conditions of the Three Gorges Reservoir area, a two-level multi-objective monitoring system was developed to monitor the atmosphere of the area. Statistical analysis of environmental monitoring data and the macro control principle were employed to configure the upper layer. The lower layer was designed by the application of the thumb rule to a local terrain and specific point sources of pollution therein. The optimized two-level system comprises an upper layer of 16 monitoring stations distributed at places of diverse geographical, ecological, economical and social characteristics, and a lower layer of ]6 sub-machines at each monitoring station of the upper layer. This optimal outcome fits the complicated conditions of the Three Gorges Reservoir area, substantially cuts down the installation cost and the operation cost, and provides accurate monitoring data of atmosphere over the entire area with a high resolution.

Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques

This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.

Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.

Air Pollution Concentration Approach to Potential Area Selection of the Air Quality Monitoring Station in Nakhon Ratchasima Municipality, Thailand

The purpose of the study is to generate traffic air information system） to determine a proper zone of AQMS （air analyzed were carbon monoxide （CO）, and nitrogen oxides （NOx） pollution map using mathematical model and GIS （geographic quality monitoring station） in municipality area. The pollutants which can be harmful to people living in the area. The three steps of mapping process were performed under the GIS environment using the existing vehicle emission rates and pollutant dispersion model. First, traffic volume, road network, and the emission rates of road segments varying with types of vehicle were collected from existing data. Second, the pollutant concentrations were calculated by use of CALINE4, a tool with Gaussian dispersion model. The model parameters include emission rate, wind directions and speeds, ambient temperature and observed pollutant concentration, and atmospheric stability during all seasons from the January 1, 2010 to May 31,2011 with regardless the rainy season. This resulted in concentrations at many receptor points along links of the road network. Third, distributions of pollution concentrations were generated by means of the spatial interpolation of those from receptors. The results of pollution raster-based maps are used for determining frequency of violence and combined pollution map. The resulting frequency of violence and intensity concentration will be further integrated to determine a potential area of AQMS. Finally, achieving pollution potential area of AQMS can be located as helpful basic data for efficient traffic and transportation planning.

Radioactivity monitoring in environmental water and air around QNPP

Results of environmental radioactivity monitoring around Qinshan Nuclear Power Plant (QNPP) are reported in this paper. From 1992 to 2005, concentrations of 90Sr, 137Cs and 3H in terrestrial freshwater are (4.4±1.7) mBq·L-1, (0.3±0.1) mBq·L-1 and (1.6±0.5) Bq·L-1, respectively, and (2.8±2.4) Bq·L-1 of 3H in rainwater. Concentrations of 90Sr, 137Cs and 3H in the seawater samples collected from sea area nearby QNPP are (5.4±4.1) mBq·L-1, (0.7±0.2) mBq·L-1 and (1.0±0.5) Bq·L-1, respectively. Concentrations of 90Sr, 137Cs and 3H in the total waste water discharged from NPP-I are (4.0±1.8) m Bq·L-1, (1.0±0.5) mBq·L-1 and (2.8±2.2) Bq·L-1, respectively, and (1.4±0.4) Bq·L-1 of 3H in seawater sampled from No.1 outlet. Atomspheric 3H concentration in 1993～2005 at two monitoring sites is (78.9±96.3) and (64.2±40.2) mBq·m-3, respectively, with an increasing trend after 2003. Atmospheric 14C concentrations at the two sites are in the same levels as the background and data of the reference site.

A Comparison of Airborne Formaldehyde Field Measurements Collected in an Anatomic Pathology Laboratory

Environmental monitoring of airborne formaldehyde (FA) using sensitive methodologies is fundamental to prevent health risks. The objective of this study was to compare three different FA monitoring methods during the daily activities of an anatomic pathology laboratory. Daily eight-hour measurements deriving from Radiello® passive diffusive samplers (PDS), NEMo XT continuous optical sensor (COS), and multi-gas 1512 photoacoustic monitor (MPM) were simultaneously compared over a period of 14 working days. Given the different daily distributions of the measurements performed by the three devices, all measurements were time-aligned for comparison purposes. The 95% limit of agreement (LOA) method was applied to estimate the degree of concordance of each device with respect to the others. Formaldehyde arithmetic mean measured using PDS was 32.6 ± 10.4 ppb (range: 19.8 - 62.7). The simultaneous measures performed by COS and MPM were respectively 42.4 ± 44.8 ppb (range: 7.0 - 175.0) and 189.0 ± 163.7 ppb (range: 40.0 - 2895.4). The MPM geometric mean (171.3 ppb) was approximately five times higher than those derived from COS (32.3 ppb) and PDS (31.4 ppb). The results of the LOA method applied to log-transformed FA data showed the same systematic discrepancies between MPM and the other two devices. A good agreement between PDS and COS could lead to a tailored approach according to the individual specificity of these techniques. This tool may be useful for accurately assessing the risk of FA exposure among healthcare workers. However, the limited specificity of the MPM does not support its use as a monitoring method for FA in the workplace.

Implementation of the Air Quality Monitoring Network at Agadir City in Morocco

Air pollution is a major global issue with widely known harmful effects on human health and the environment. This pollution is a very complex phenomenon given the diversity of pollutants that may be present in the atmosphere. The air quality in urban areas is of a great concern for residents living in cities and represents a current issue that requires an adequate management. So that air quality policy is driven by health concerns. In this paper, we present an overview on the experience of Agadir city to establish the air quality management policy, local authority on the whole have developed a good understanding of air quality in the area. Indeed for several years, efforts have been made to monitor the air quality in this city, this translated by air quality assessment since 2006 using mobile laboratory and fixed station. Our goals in this study were to review the operation of Local Air Quality Management (LAQM) making better use of available resources to improve its outcomes and make recommendations with a view to improving air quality issues. This work highlights the requirement to revise periodically the LAQM for generating priority for air quality issues within local authority and the need to implement the optimizing Air Quality Monitoring Network (AQMN).

Real-Time Air Monitoring of Trichloroethylene and Tetrachloroethylene Using Mobile TAGA Mass Spectrometry

Trichloroethylene (TCE) is a chlorinated liquid that is commonly used for metal degreasing, household and industrial dry cleaning, and in paints and glues. Tetrachloroethylene, also known as perchloroethylene (PCE), is an excellent solvent for organic materials. PCE is volatile, highly stable, non-flammable and widely used in dry cleaning. A new method has been developed for measuring TCE and PCE in ambient air in real-time. Based upon the chemical fingerprinting and concentration levels, the method was able to isolate the source of the emissions to the responsible facility. Real-time monitoring was accomplished by utilizing a low pressure chemical ionization source (LPCI) interfaced to a tandem mass spectrometer (TAGA). Monitoring the response of specific parent/daughter ion pairs, the TAGA was used to measure concentrations of TCE and PCE. By optimizing various TAGA parameters, detection limits (DL) as low as 0.5 μg/m3 was achieved for TCE and PCE. Unlike methods using cartridge sampling and GC/MS analysis, this new method provides a real time measurement for a wide range of TCE and PCE concentrations. This unique method was applied in 2000 and 2002 to measure TCE emitted from a manufacturer of stainless steel tubing in Eastern Ontario. The maximum half-hour average concentration of TCE measured downwind of the facility was 1300 μg/m3 and the maximum instantaneous level was measured at 115,000 μg/m3. The information collected by the TAGA unit was used by the Standard Development Branch of Ontario Ministry of the Environment to adopt the half-hour Point of Impingement (POI) standard of TCE to be 36 μg/m3 in 2010. This method successfully identified and simultaneously measured TCE and PCE during a 2011 air monitoring survey of a hazardous waste disposal and treatment facility in Southern Ontario.

Low-Cost Sensors Calibration for Monitoring Air Quality in the Federal District—Brazil

Critical situations that cannot be solved by conventional approaches (traditional air quality monitoring networks), have the possibility of being managed quickly by a wide network of portable systems with sensors. The purpose of this research was to calibrate and validate low-cost sensors. Pilot indoor and outdoor areas, in the central area of Brasilia (Brazil’s capital city) were chosen for corporative performance evaluation of the sensors. The CO at 99.999% volumetric injection method has been used in a gas test box, among two MiCS-5521 (CO/VOC) sensors, one being new and the other one with a short useful life. The number of injections adopted to each volume (from 1 ml to 6 ml) was 10, rising each sensor’s confidence interval mean. A increase of the injected volume (ml) of CO resulted in significant decrease in a resistance (Ohms), as shown by a good inverse relationship on the interaction of these two variables (r = 0.88), with good measurement accuracy, when compared to the manufacturer’s reference datasheet. Finally, a geospatial management system was built for the pollution data measured by the low-cost sensors.

Monitoring Ambient Air Quality in the Carpathian Region of Ukraine

Ivano-Frankivsk oblast is located in the south-western part of Ukraine, close to the geographical center of Europe at the junction of the two major geographic units, the East European Plain and the Eastern Carpathians （the Carpathian region of Ukraine）. Between September 2013 and September 2015, the expert group of the Department of Organic and Analytical Chemistry of Vasyl Stefanyk Precarpathian National University conducted the mobile monitoring of air cleanliness in Ivano-Frankivsk region; the authors monitored the level of air contamination with seven gases： carbon monoxide and dioxide, sulfur dioxide, nitrogen dioxide, hydrogen sulfide, ammonia, formaldehyde, beta-particles, and gamma radiation. For this purpose, there were used six automatic analyzers ＂Dozor-C-P＂, the formaldehyde Gas Analyzer ＂MIC-98170＂, and the radiometer ＂TERRA＂. The monitoring resulted in the making of three maps of Ivano-Frankivsk region, on which the average levels of concentration of three particular gases （CO2, SO2, and CH20） throughout all the districts of the oblast were marked. There was registered the excess of CH20 limits--the consequences of the presence of the chipboard and wood-fiber production, for which formaldehyde is used as a raw material. Also there was observed the excess of SO2 in the air caused by gas fumes from vehicles and thermal power stations.

Hybrid Deep Learning Enabled Air Pollution Monitoring in ITS Environment

Intelligent Transportation Systems(ITS)have become a vital part in improving human lives and modern economy.It aims at enhancing road safety and environmental quality.There is a tremendous increase observed in the number of vehicles in recent years,owing to increasing population.Each vehicle has its own individual emission rate;however,the issue arises when the emission rate crosses a standard value.Owing to the technological advances made in Artificial Intelligence(AI)techniques,it is easy to leverage it to develop prediction approaches so as to monitor and control air pollution.The current research paper presents Oppositional Shark Shell Optimization with Hybrid Deep Learning Model for Air Pollution Monitoring(OSSOHDLAPM)in ITS environment.The proposed OSSO-HDLAPM technique includes a set of sensors embedded in vehicles to measure the level of pollutants.In addition,hybridized Convolution Neural Network with Long Short-Term Memory(HCNN-LSTM)model is used to predict pollutant level based on the data attained earlier by the sensors.In HCNN-LSTM model,the hyperparameters are selected and optimized using OSSO algorithm.In order to validate the performance of the proposed OSSO-HDLAPM technique,a series of experiments was conducted and the obtained results showcase the superior performance of OSSO-HDLAPM technique under different evaluation parameters.

Development of the Program of Monitoring of Composition of Atmospheric Air

To settle the indicators of air pollutions at the industrial enterprises is very important. For this purpose, authors analyzed and developed a program for monitoring atmospheric composition in Borland Delphi 7 for the calculation of the hazard class and the average daily maximum allowable concentration of air. Results of calculations on this program will allow operating composition of air by regulation of operating modes of filtering devices.

Investigating the Air Quality Parameters in Louisiana’s Industrial Corridor: A Baton Rouge Case Study

In a local context, sustainable development entails utilizing the current resources—material and immaterial, measurable and immeasurable, popular and unpopular—of the community in a manner that avoids overexploitation and ensures intergenerational equity. This approach prioritizes the safety and health of local citizens, placing communal productivity above corporate profitability. This research aims to assess air quality surrounding 28 chemical industry sites in Baton Rouge, Louisiana, to understand the environmental and health impacts of industrial pollutants, with a focus on environmental justice. Air quality pollutants, including PM2.5, PM10, O3, NO2, CO, and SO2, were monitored for 75 days during the Summer, using the BreezoMeter app. Python, Mapize, and QGIS software technologies were utilized for data analysis and visualization. Findings indicate a reduction in NO2 and CO levels, compared to existing literature. However, the persistent challenge of particulate matter suggests areas for further environmental management efforts. Additionally, the research suggests a significant disparity in air pollution exposure, probably affecting marginalized communities. Although the nature of the study might not fully capture annual pollution trends, the findings highlight the urgent need for the chemical industry to adopt efficient production methods and for policymakers to enhance air quality standards and enforcement, particularly in pollution-sensitive areas. The disproportionate impact of air pollution on vulnerable communities calls for a more inclusive approach to environmental justice, ensuring equitable distribution of clean air benefits and community involvement in pollution management decisions.

Air Quality Monitoring and Its Implication on the Environmental Licensing Process in Brazil

In the state of Sao Paulo, Brazil, public policies regarding the air quality aimed at the welfare of the population are strongly dependent on monitoring conducted by the Sao Paulo State Environmental Company (CETESB), which can be influenced by faulty monitors and equipment support and cuts in power supply, among others. A research conducted from 1998 to 2008 indicated that a significant portion of the air quality automatic stations in the state of Sao Paulo did not meet the criterion of representativeness of measurements of PM10, NO2, O3, CO and SO2 concentrations which resulted in the classification of some municipalities as the nonattainment area, a situation evidenced for PM10 and O3 parameters. The network unavailability for each parameter was estimated and compared with the monitoring networks operated in Canada and the UK. This paper discusses the implications of the lack of representativeness of measurements in the environmental licensing process of pollution sources from 2008, when by the effect of state law, municipalities have been qualified according to their air quality nonattainment level.

An Optical Fiber Sensor Probe Using a PMMA/CPR Coated Bent Optical Fiber as a Transducer for Monitoring Trace Ammonia

Ammonia sensors have broad spectrum of applications for industrial process control as well as for environ-mental monitoring. An optical fiber ammonia sensor probe has been developed by using a bent optical fiber having dual poly(methyl methacrylate) (PMMA)/chlorophenol red (CPR) coatings as a transducer. This sen-sor probe was tested for monitoring trace ammonia in gas samples using air as sample matrix. The reaction of ammonia with CPR causes a color change of the reagent, which was detected by using fiber optic evanes-cent wave absorption spectrometry as a sensing signal. By adopting a dual layer coating structure, the sensor probe has faster response compared to a sensor using a broadly accepted sensing reagent-immobilized poly-mer coating structure. The sensor developed in this work is sensitive, has a detection limit of 2.7 ppb NH3 in air, which is the most sensitive among the reported optical fiber ammonia sensors to the best knowledge of the authors. The sensor is also reversible and has a response time of 25 minutes. The features of high sensi-tivity, reversibility and reasonable response time make this sensor technique very attractive for air quality monitoring.

Monitoring of Ultrafine Particles in the Surrounding Urban Area of In-Land Passenger Ferries

Maritime transportation, widely used both in international transport of goods and touristic purposes, has been identified as a significant source of ultrafine particles (UFP). In-land passenger ferry is a source of UFP far less addressed;however, in locations with relatively high frequency of this transportation mode, it is expected that they contribute to an increase of their concentration. Moreover, the negative effects of UFP on human health and environment are known and therefore, monitoring UFP produced by ferries is vital to assess the degree of exposure of who work or live close to ferries’ terminals or downwind to their cruising path. This work aims to study the influence of in-land ferries activities on UFP in the urban/suburban areas near ferries’ terminals and downwind across the cruising path. The UFP monitoring campaign was carried out from September to December 2018 for 19 non-consecutive periods. The sampling sites were chosen in order to maximize measurements under downwind conditions and allow the association between ferry operation and UFP concentration response. Based on data collected, correlation analysis was performed between ferry’s traffic and particle number counting (PNC) of UFP, and also with meteorological variables. Results show an increase in PNC ranging from 25 to 197% during the third minute around a ferry movement and are moderate to positive significant correlations between PNC values and the number of ferry operations (r = 0.79 to r = 0.94), showing that UFP emitted by in-land ferries contributes to PNC increase. Moreover, negative correlations (r = -0.85 to r = -0.93) between PNC and wind intensity were also found.

Monitoring of Atmospheric Pollutant Concentrations in the City of Dakar, Senegal

The lack of data on air quality monitoring and neglected and overlooked pollutant emissions in the transportation and industrial sectors are motives for the government of Senegal to set up, in 2009, an air quality management center, the CGQA (Centre de Gestion de la Qualité de l’Air). Air quality monitoring at CGQA deals with mainly six pollutants: carbon monoxide (CO), nitrogen oxides (NOx) (with nitrogen dioxide (NO2) and nitrogen monoxide (NO)), sulfur dioxide (SO2), ground-level ozone (O3), Benzene-Toluene-Xylenes (BTX), and particulate matters (PM) with diameters less than 10 μm (PM10) and 2.5 μm (PM2.5). The concentration levels of gaseous pollutants (CO, O3, NO2 and SO2) in the city generally remain below the limit value set by the WHO (World Health Organization). However, particulate matters (PM10 and PM2.5) are the most important pollutants observed in Dakar, they far exceed the annual thresholds set by the WHO and the national standard (NS 05-062). This situation results in an Air Quality Index (AQI) around bad and very bad during the dry season (November to May) and good to moderate during the rainy season (June to October). The concentrations of PM10 and PM2.5 vary respectively from 120 to 180 μg·m-3 and from 25 to 48 μg·m-3. The average concentrations of pollutants therefore vary from one area to another and depending on the location of the air quality monitoring station (near industrial sites, traffic, etc.).