Air Pollution Monitoring Research Articles

IoT platform enhanced with neural network for air pollutant monitoring

IoT platform enhanced with neural network for air pollutant monitoring

The impact of restriction measures implementation due to COVID-19 on particulate atmospheric pollution in Greek urban areas

In this work, the variation on the levels of suspended particulates, in Greek urban areas, was examined, for the period of the restrictive measures implementation (period 1: March to May 2020 and period 2: November 2020 to January 2021) due to the pandemic caused by the COVID-19 virus. The PM10 and PM2.5 concentrations are measured by the National Air Pollution Monitoring Network of Greece monitored by the Energy/Environment Ministry. In particular, a comparison of the PM10 and PM2.5 concentration levels was made, concerning the above time periods, with the corresponding atmospheric pollution levels during the periods 2001-2009 (period before the Greek economic crisis), 2010-2019 (period during the Greek economic crisis), as well as with the entire time period 2001-2019. The assessment showed a significant reduction in the levels of both PM10 concentrations, which ranged from 8% to 45%, and PM2.5 concentrations, which ranged from 13% to 48%, thus demonstrating the effectiveness of the control measures in improving air quality in Greek urban areas. The above findings show that with appropriate emission control strategies, vital improvements in the air environment of urban areas can be achieved.

Mortality burden attributable to exceptional PM2.5 air pollution events in Australian cities: A health impact assessment

BackgroundPeople living in Australian cities face increased mortality risks from exposure to extreme air pollution events due to bushfires and dust storms. However, the burden of mortality attributable to exceptional PM2.5 levels has not been well characterised. We assessed the burden of mortality due to PM2.5 pollution events in Australian capital cities between 2001 and 2020. MethodsFor this health impact assessment, we obtained data on daily counts of deaths for all non-accidental causes and ages from the Australian National Vital Statistics Register. Daily concentrations of PM2.5 were estimated at a 5 km grid cell, using a Random Forest statistical model of data from air pollution monitoring sites combined with a range of satellite and land use-related data. We calculated the exceptional PM2.5 levels for each extreme pollution exposure day using the deviation from a seasonal and trend loess decomposition model. The burden of mortality was examined using a relative risk concentration-response function suggested in the literature. FindingsOver the 20-year study period, we estimated 1454 (95 % CI 987, 1920) deaths in the major Australian cities attributable to exceptional PM2.5 exposure levels. The mortality burden due to PM2.5 exposure on extreme pollution days was considerable. Variations were observed across Australia. Despite relatively low daily PM2.5 levels compared to global averages, all Australian cities have extreme pollution exposure days, with PM2.5 concentrations exceeding the World Health Organisation Air Quality Guideline standard for 24-h exposure. Our analysis results indicate that nearly one-third of deaths from extreme air pollution exposure can be prevented with a 5 % reduction in PM2.5 levels on days with exceptional pollution. InterpretationExposure to exceptional PM2.5 events was associated with an increased mortality burden in Australia's cities. Policies and coordinated action are needed to manage the health risks of extreme air pollution events due to bushfires and dust storms under climate change.

Neighborhood-Level Air Pollution Monitoring and Analysis in Massachusetts

Air pollution is a significant environmental issue with far-reaching consequences for both human health and the planet. It arises from the emission of harmful pollutants, such as particulate matter, ozone, and nitrogen dioxide, into the atmosphere. These pollutants originate from various sources, including vehicles, industrial facilities, and power plants. The impacts of air pollution are wide-ranging, encompassing respiratory infections, cardiovascular disease, cancer, harm to ecosystems, and contributions to climate change. The study of air pollution is a multidisciplinary field that draws upon knowledge from physics, chemistry, biology, and engineering. Understanding the causes and effects of air pollution is crucial for developing effective strategies to mitigate its harmful effects. Physics, in particular, plays a vital role in this field by providing the tools and techniques required to measure and comprehend the behavior of pollutants. By examining air pollution, we gain insights into the factors driving this problem and can devise measures to reduce pollution levels. Moreover, this knowledge empowers individuals and societies to make informed decisions about minimizing exposure to pollutants and formulating policies and regulations that safeguard both human well-being and the environment. In our study conducted in the central Massachusetts region, we investigated the status of air pollution using a combination of Environmental Protection Agency (EPA) monitoring sites and hand-held sensors. While the EPA sites offer longterm monitoring data, hand-held devices’ flexible and affordable nature allowed us to explore air quality at the local neighborhood and street levels. By utilizing these tools, we assessed the spatial and temporal variations in air pollution within the city, aiding in the identification of localized hotspots. Such information is valuable for targeting specific areas requiring interventions and further understanding the dynamics of pollution distribution.

GeoAI Dataset for Industrial Park and Quarry Classification from KOMPSAT-3/3A Optical Satellite Imagery

Air pollution is a serious problem in the world, and it is necessary to monitor air pollution emission sources in other neighboring countries to respond to the problem of air pollution spreading across borders. In this study, we utilized domestic and international optical images from KOMPSAT-3/3A satellites to build an AI training dataset for classifying industrial parks and quarries, which are representative sources of air pollution emissions. The data can be used to identify the distribution of air pollution emission sources located at home and abroad along with various state-of-the-art models in the image segmentation field, and is expected to contribute to the preservation of Korea’s air environment as a basis for establishing air-related policies.

Using Mobile and Fixed Internet of Things Sensing Networks, Real-Time Monitoring and Prediction of Air Quality in One\'s Immediate Vicinity

Abstract: A significant global problem has emerged in several locations as a result of air pollution and its adverse impact on human well-being. Recently, there has been a rise in the number of researchers that are keen on evaluating and forecasting the air quality in close proximity to individuals. The use of the Internet of Things (IoT) across several businesses has significantly enhanced people's quality of life by interconnecting multiple sensors in diverse locations. Moreover, the Internet of Things (IoT) has streamlined the task of monitoring air pollution. The conventional utilization of stationary sensors is inadequate for acquiring an accurate and all-encompassing representation of the air pollution levels in close proximity to people. This is due to the fact that the sensors in closest proximity to people may be located many kilometers apart from each other. The objective of our study is to construct a model that precisely depicts the air quality pattern within a certain geographic area. This objective will be accomplished by using a combination of stationary and portable Internet of Things sensors. The sensors are affixed to vehicles that are carrying out surveillance in the vicinity. Our methodology allows for a thorough examination of the whole spectrum of air quality fluctuations in neighboring regions. Through the use of many machine learning algorithms and realworld data, we showcase the efficacy of our methodology in accurately discerning and forecasting air quality without compromising on accuracy. The results of our research indicate that there is significant potential for efficiently monitoring and forecasting air quality in the context of a smart city.

Existing and alternative air pollution monitoring platforms in Nepal: A systematic review

Air pollution has become one of the most significant threats to the well-being of humanity and the environment in many developing countries, including the Himalayan nation of Nepal. The use of both stationary and mobile instrumentation platforms for environmental and atmospheric research has witnessed a significant growth globally in the last two decades. In this review, we explore the current status of air quality monitoring infrastructure as well as a future scope of using new platforms using low-cost air sensors for pollution mapping in Nepal. While large-scale regional dynamics of air pollution in the country have been previously studied using both experimental and modeling-based approaches, usability of low-cost instrumentation platforms in Nepal have not been explored by past studies. From the retrospective discussions regarding the past studies, we discuss here the data needs that can be fulfilled with low-cost measurements using land-based, automotive, aerial and satellite-based platforms. The importance of hyper-local air quality monitoring is highlighted in the context of Nepal, followed by the identification of recent advancements in sensor technologies, available facilities and infrastructure in Nepal.

Monitoring Emissions over Coastal Regions of Pakistan: Current Status and Applicable Techniques

Shipping operations have an environmental impact both in ports, as well as in the immediate vicinity of the coast. These impacts are due to the fuel burning exhaust from the ship’s main and auxiliary engines which contain gases like Carbon Dioxide (CO2), Carbon Monoxide (CO), Nitrogen Dioxide (NO2) and Sulphur Dioxide (SO2) and machinery used for loading and unloading, in addition to particulate matter and dust from the handling of materials such as grain, coal and sand. Remote Sensing (RS) and Geographic Information System (GIS) is an efficient tool being used worldwide for the monitoring of environmental problems. Response of Earth’s atmosphere or surface to electromagnetic radiation can be mapped through RS for the status of changes in the natural environment. Smart sensors developed for detection of air pollutants is an emerging technique to monitor emissions for real time monitoring and management of air pollution in distant regions. In the current study, satellite data is used to investigated concentrations of key emissions from shipping operations at ports to analyse the status of air pollutants over coastal regions of Pakistan. The pollutants investigated in this study through RS and GIS technique include CO2, SO2, NO2, CO and Particulate Matter (PM). These emission trends can be used to determine the efficacy of mitigatory measures to reduce emissions at ports and monitor the air quality of coastal areas. Furthermore, use of scientific technology and current trends in the measurement and monitoring of shipping emission have been studied for their applicability in Pakistan.

IoT sensor nodes for air pollution monitoring: A review

Air quality is an important environmental concern, as it is strictly related to human health risks and adverse eff ects on it. Monitoring air pollutants and diff erent ancillary parameters is a feasible and crucial approach to address this challenge. This task typically involves high expenses in case measurements are carried out by using conventi onal measurement instruments and human operators. However, uti lizing measurement systems with low-cost sensors can reduce the overall implementati on eff ort. The aim of this paper is to describe the sensor node architecture applicable to a general monitoring system and based on this structure, review diff erent current low-cost measurement system proposals for outdoor air quality monitoring.

An Analytical Model for Estimating Ship-Related Emissions in Port Areas

Intensive shipping activity in port areas is considered one of the leading problems in the maritime sector, which has a negative effect on climate change and local air quality. The compilation of detailed inventories of combustion gases released by ships should therefore provide a more accurate overview of emission levels, which can serve as a basis for analysing impacts on the port community and lead to the establishment of better environmental measures. Thus, the aim of this study was to develop an adaptable and relevant analytical model capable of integrating a comprehensive methodology with large databases of ship movements and technical details to provide clear ship-related emission estimates in large port areas. Considering the lack of research in Croatia that includes the mentioned approach and the insufficient monitoring of air pollutants in ports, the model was used to produce an initial overall emissions inventory for the Port of Split, the busiest passenger port in Croatia. In the model, bottom-up logic with an energy-based method was applied to detailed technical and near-real-time shipping data from AIS, creating the first high-density spatial and temporal overview of shipping emissions in the City port basin. The results showed strong seasonal fluctuations and large discrepancies in the quantities emitted between different ship types and operating modes. The analysis therefore raised the question of the need for the future development and implementation of a scalable system that would provide a more transparent and efficient overview of the important characteristics of air pollution from ships and port areas.

Unmanned Aerial Vehicles for Air Pollution Monitoring: A Survey

Unmanned Aerial Vehicles for Air Pollution Monitoring: A Survey

Spatial variation of air pollutants by using GIS modelling

<p>Air pollution in Tamil Nadu has become a pressing issue, as various pollutants have surpassed the limits established by the pollution control board. The major air pollutants in the region, namely sulphur dioxide, nitrogen dioxide, and suspended particulate matter, have been identified. This study aims to monitor and analyse the levels of air pollution in Tamil Nadu, with a specific focus on key parameters such as SO2, NO2, PM2.5, PM10, and ozone. By examining the atmospheric levels of these pollutants and their correlation with temperature fluctuations, the impact on human health and respiratory problems can be evaluated. Moreover, the study will assess the contribution of traffic emissions and vehicular pollution to the concentration of PM10 in different areas. Additionally, GIS technology will be utilized to spatially and temporally investigate pollution concentrations and evaluate their effect on the air quality index. By comparing the pollution concentration from thermal power stations and vehicular pollution, the study will identify the sources of pollutants, with a particular emphasis on SO2 and NOx emissions from vehicles and higher levels of suspended particulate matter from thermal stations. The analysis of these pollutants and their sources will provide valuable insights for the implementation of effective pollution control measures and the management of environmental pollution in Tamil Nadu. Given the concerns regarding pollutants exceeding the prescribed limits set by the pollution control board, monitoring air pollution in Tamil Nadu is of utmost importance.</p>

Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020

The pursuit of higher-resolution and more reliable spatial distribution simulation results for air pollutants is important to human health and environmental safety. However, the lack of high-resolution remote sensing retrieval parameters for gaseous pollutants (sulfur dioxide and ozone) limits the simulation effect to a 1 km resolution. To address this issue, we sequentially generated and optimized the spatial distributions of near-surface PM2.5, SO2, and ozone at a 1 km resolution in China through two approaches. First, we employed spatial sampling, random ID, and parameter convolution methods to jointly optimize a tree-based machine-learning gradient-boosting framework, LightGBM, and improve the performance of spatial air pollutant simulations. Second, we simulated PM2.5, used the simulated PM2.5 result to simulate SO2, and then used the simulated SO2 to simulate ozone. We improved the stability of 1 km-resolution SO2 and ozone products through the proposed sequence of multiple-pollutant simulations. The cross-validation (CV) of the random sample yielded an R2 of 0.90 and an RMSE of 9.62 µg∙m−3 for PM2.5, an R2 of 0.92 and an RMSE of 3.9 µg∙m−3 for SO2, and an R2 of 0.94 and an RMSE of 5.9 µg∙m−3 for ozone, which are values better than those in previous related studies. In addition, we tested the reliability of PM2.5, SO2, and ozone products in China through spatial distribution reliability analysis and parameter importance reliability analysis. The PM2.5, SO2, and ozone simulation models and multiple-air-pollutant (MuAP) products generated by the two optimization methods proposed in this study are of great value for long-term, large-scale, and regional-scale air pollution monitoring and predictions, as well as population health assessments.

Air pollution mortality benefits of sustained COVID-19 mobility restrictions in Australian cities

ObjectivesEmissions from road traffic, power generation and industry were substantially reduced during pandemic lockdown periods globally. Thus, we analysed reductions in traffic-related air pollution in Australian capital cities during March–April 2020 and then modelled the mortality benefits that could be realised if similar reductions were sustained by structural policy interventions. Study designSatellite, air pollution monitor and land use observations were used to estimate ground-level nitrogen dioxide (NO2) concentrations in all Australian capital cities during: (a) a typical year with no prolonged air pollution events; (b) a hypothetical sustained reduction in NO2 equivalent to the COVID-19 lockdowns. MethodsWe use the WHO recommended NO2 exposure-response coefficient for mortality (1.023, 95 % CI: 1.008–1.037, per 10 μg/m3 annual average) to assess gains in life expectancy and population-wide years of life from reduced exposure to traffic-related air pollution. ResultsWe attribute 1.1 % of deaths to anthropogenic NO2 exposures in Australian cities, corresponding to a total of 13,340 years of life lost annually. Although COVID-19-related reductions in NO2 varied widely between Australian cities during April 2020, equivalent and sustained reductions in NO2 emissions could reduce NO2-attributable deaths by 27 %, resulting in 3348 years of life gained annually. ConclusionsCOVID-19 mobility restrictions reduced NO2 emissions and population-wide exposures in Australian cities. When sustained to the same extent by policy interventions that reduce fossil fuel consumption by favouring the uptake of electric vehicles, active travel and public transport, the health, mortality and economic benefits will be measurable in Australian cities.

TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS

Abstract. Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3.

Statistic Study of Gaseous Air Contamination in the City of Svishtov, Bulgaria

Polluted air has a negative impact on human health and plant and animal life. There are two types of sources of air pollution: human activity (transport, heating, industry and others) and natural phenomena such as natural fires, volcanic activity, deforestation and others. Substances that are the most common air pollutants are dust particles, carbon and nitrogen dioxides, carbon and nitrogen oxides, hydrocarbons; aldehydes; radioactive substances, heavy metals, sulfur dioxide, ozone, etc. Gaseous air pollutants of primary concern in urban environments include sulfur dioxide, nitrogen dioxide, and carbon monoxide. Sulfur dioxide is a colorless gas that is a product of volcanic eruptions and various industrial processes. It’s usually released when coal and oil are burned. Nitrogen oxides, especially nitrogen dioxide, are poisonous gases with a pungent, suffocating odor. There are many regulations aimed at air pollution and pollution monitoring for all European countries. Bulgaria, as part of the EU, must comply with all these regulations. There are still many places and periods of time in Bulgaria over the years with air pollution greater than daily norms. The present study is dedicated to the study of air pollution with gaseous substances in the area of Svishtov, Bulgaria. The town of Svishtov is located in Northern Bulgaria, on the banks of the Danube River, where the southernmost point of the river along its entire length is located. A statistical analysis of the level of air pollution in Svishtov based on official data from the monitoring station in the city is presented. The measurements cover the period 2021 - 2023. All research results are presented graphically and commented.

ZnAs2O4′s gas sensing performance

Recently, there has been a lot of discussion about the significance of new materials, such as gas sensing mixed metal oxides (MMO), in monitoring air pollution, home security, public safety, hazardous industrial emissions, and automobile exhaust. The catalyst, ZnAs2O4, was synthesized via a mechanochemical method (MCh). FTIR, UV-DRS, XRD, and SEM were used to characterize this synthesized catalyst. The average particle size has been identified to be between 25 and 30 nm. ZnAs2O4′s gas sensing capabilities were investigated in relation to NH3, CO, H2S and Cl2. ZnAs2O4 material has been found to have considerable gas sensing properties toward highly harmful gases, including H2S gas emitted by sewage plants and the oil and natural gas industry. It also has superior selectivity, increased sensitivity, and a moderate temperature requirement.

Smart Environment: AI-Driven Predictions and Forecasting of Air Quality

Addressing the critical issue of air quality in the Coimbatore region, this study introduces a novel approach for continuous monitoring and forecasting of air pollution. By utilizing the Internet of Things (IoT) technology integrated with Artificial Intelligence (AI) methods, this research focuses on monitoring and forecasting three major pollutants such as Ozone (O3), Ammonia (NH3), and Carbon Monoxide (CO). The proposed IoT-based sensor nodes collect the real-time data and give the resultant data as an input to the Naive Bayes (NB) for classification and Auto-Regression Integrating Moving Average (ARIMA) for optimization. The optimized model parameters are obtained and then validated by using performance metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Deploying a machine learning algorithm on a Raspberry Pi-3, the proposed system ensures efficient monitoring and forecasting of air pollutants 24/7 through an online open-source dashboard.

Environmental exposure and sensitization patterns in a Swiss alpine pediatric cohort

BackgroundThe level of environmental exposure throughout life may contribute to the prevalence of allergic sensitization and allergic disease. The alpine climate has been considered a healthy climate with little allergen exposure and pollution. We conducted a cross-sectional study to investigate local environmental exposure and concomitant prevalence of allergic sensitization among local school children born and raised in an alpine environment. MethodsClinical and demographic data were collected with a questionnaire. Allergen content was assessed in residential settled dust samples, lifetime exposure to pollen and air pollution was calculated using data from national pollen and air pollution monitoring stations, and the allergic sensitization profile was determined with component resolved diagnostics (ISAC®). Univariate and multivariate regression models were used to estimate the relation between exposure and sensitization. ResultsIn a cohort of children born and raised in an alpine environment, sensitization to aeroallergens is quite common (38%), especially to grass (33%) and cat (16%). House dust mite allergen was detected in up to 38% of residential dust samples, but sensitization to HDM was low (2.5%). Pollutant levels were low, but an increasing trend was observed in the amount of ozone and PM10. Living close to a busy road was associated with increased odds OR (95% CI) for being sensitized to any allergen 2.7 (1.0–7.2), to outdoor allergens 2.8 (1.1–7.1) and being sensitized plus reporting symptoms of rhinoconjunctivitis 4.4 (1.3–14.8) and asthma 5.5 (1.4–21). Indoor living conditions, including the presence of visible mold, increased the odds of being sensitized to indoor allergens (1.9 (1.1–3.2) and being sensitized plus reporting symptoms of rhinoconjunctivitis 1.9 (1.0–3.6) and asthma 2.1 (1.0–4.1). ConclusionIn a healthy alpine environment, pollution might still be an important factor contributing to allergic sensitization.

Five Years of Accurate PM2.5 Measurements Demonstrate the Value of Low-Cost PurpleAir Monitors in Areas Affected by Woodsmoke

Low-cost optical sensors are used in many countries to monitor fine particulate (PM2.5) air pollution, especially in cities and towns with large spatial and temporal variation due to woodsmoke pollution. Previous peer-reviewed research derived calibration equations for PurpleAir (PA) sensors by co-locating PA units at a government regulatory air pollution monitoring site in Armidale, NSW, Australia, a town where woodsmoke is the main source of PM2.5 pollution. The calibrations enabled the PA sensors to provide accurate estimates of PM2.5 that were almost identical to those from the NSW Government reference equipment and allowed the high levels of wintertime PM2.5 pollution and the substantial spatial and temporal variation from wood heaters to be quantified, as well as the estimated costs of premature mortality exceeding $10,000 per wood heater per year. This follow-up study evaluates eight PA sensors co-located at the same government site to check their accuracy over the following four years, using either the original calibrations, the default woodsmoke equation on the PA website for uncalibrated sensors, or the ALT-34 conversion equation (see text). Minimal calibration drift was observed, with year-round correlations, r = 0.98 ± 0.01, and root mean square error (RMSE) = 2.0 μg/m3 for daily average PA PM2.5 vs. reference equipment. The utitilty of the PA sensors without prior calibration at locations affected by woodsmoke was also demonstrated by the year-round correlations of 0.94 and low RMSE between PA (woodsmoke and ALT-34 conversions) and reference PM2.5 at the NSW Government monitoring sites in Orange and Gunnedah. To ensure the reliability of the PA data, basic quality checks are recommended, including the agreement of the two laser sensors in each PA unit and removing any transient spikes affecting only one sensor. In Armidale, from 2019 to 2022, the continuing high spatial variation in the PM2.5 levels observed during the colder months was many times higher than any discrepancies between the PA and reference measurements. Particularly unhealthy PM2.5 levels were noted in southern and eastern central Armidale. The measurements inside two older weatherboard houses in Armidale showed that high outdoor pollution resulted in high pollution inside the houses within 1–2 h. Daily average PM2.5 concentrations available on the PA website allow air pollution at different sites across regions (and countries) to be compared. Such comparisons revealed major elevations in PA PM2.5 at Gunnedah, Orange, Monash (Australian Capital Territory), and Christchurch (New Zealand) during the wood heating season. The data for Gunnedah and Muswellbrook suggest a slight underestimation of PM2.5 at other times of the year when there are proportionately more dust and other larger particles. A network of appropriately calibrated PA sensors can provide valuable information on the spatial and temporal variation in the air pollution that can be used to identify pollution hotspots, improve estimates of population exposure and health costs, and inform public policy.