Pollution Mapping Research Articles

Long-Range Network of Air Quality Index Sensors in an Urban Area.

In recent times the escalating pollution within densely populated metropolitan areas has emerged as a significant and pressing concern. Authorities are actively grappling with the challenge of devising solutions to promote a cleaner and more environmentally friendly urban landscapes. This paper outlines the potential of establishing a LoRa node network within a densely populated urban environment. Each LoRa node in this network is equipped with an air quality measurement sensor. This interconnected system efficiently transmits all the analyzed data to a gateway, which subsequently sends it to a server or database in real time. These data are then harnessed to create a pollution map for the corresponding area, providing users with the opportunity to assess local pollution levels and their recent variations. Furthermore, this information proves valuable when determining the optimal route between two points in the city, enabling users to select the path with the lowest pollution levels, thus enhancing the overall quality of the urban environment. This advantage contributes to alleviating congestion and reducing excessive pollution often concentrated behind buildings or on adjacent streets.

What Do We Know About Plastic Pollution in Coastal/Marine Tourism? Documenting Its Present Research Status from 1999 to 2022

This study aims to present a comprehensive knowledge mapping of plastic pollution on coastal/marine tourism research. To achieve this objective, the present study employed a two-tier analysis, including a systematic review of literature and bibliometric analysis of 164 scholarly articles spanning 1999 to 2022. The results underline the multidisciplinary nature of this research domain, characterized by a significant upswing in publications. The dominant countries in this field are identified as Brazil, Australia, and the USA. A noteworthy collaboration dynamic between the Chinese Academy of Sciences, East China Normal University, and the University of Chinese Academy of Sciences has emerged, signaling opportunities for enhanced cross-border cooperation. Jambeck, who provided data support for this research field, is found as the most cited author. Marine Pollution Bulletin, Environmental Pollution, and Science of the Total Environment are the most prolific journals discussing such topics. The primary research clusters in this field include seasonal comparison, Qinghai Lake, and small plastic debris. Additionally, scholars have recently focused on new topics such as seasonal comparison, case study, spatial pattern, and the Southern Baltic Sea in the last 3 years. This research also reports 10 articles with the highest citations and 10 highly cited papers. Not only this, the present study presents the inaugural bibliometric analysis on plastic pollution in coastal and marine tourism. To the authors’ knowledge, it stands as a pioneering investigation of its nature, offering a comprehensive view on the research domain by evaluating a time span exceeding two decades, with a specific emphasis on the literature pertaining to marine tourism.

Monitoring and Control of Particle Matter Concentration’s Traffic-Related Air Pollution Using Low-Cost Mobile Sensors at Six Intersections in Yaoundé, Cameroon

Particle pollution has a major influence on public health in Central African cities. Measuring the levels of pollution to which populations are exposed is difficult because only a few African countries have an air quality monitoring network in place. Yet, given the specific anthropogenic sources prevalent in African countries, as well as the predicted increase in their emissions in the next few years if no laws are implemented, solutions must be developed. The methodology utilised in this study allows diverse research teams to estimate the population's exposure levels to urban particle pollution at a lower cost. In this investigation, we collected data concentrations using Air Master. The spatial mapping of pollutants shows that the Mvog-Mbi crossroad and the Education crossroad are the most polluted points among the six measurement points, with PM2.5 concentrations around 145–170 µg.m-3. Regarding PM10 and PM1, the Mvog-Mbi crossroads is the point where concentrations are highest, fluctuating respectively between 150 and 180 µg.m-3, 41 and 48 µg.m-3. At some locations, the particle ratio exceeds the WHO recommended range of 0.5–0.8 (Mvog-Mbi Crossroads, Education Crossroads, Poste Centrale, Hilton). These are the high-risk zones for those with respiratory difficulties. At some locations, the particle ratio exceeds the WHO recommended range of 0.5–0.8 (Mvog-Mbi crossroads, education crossroads, Poste Centrale, Hilton). Regardless of particle size, these pollutants in air readings are greater and beyond the World Health Organization's recommended values. Furthermore, this investigation provides critical information on Yaoundé pollution levels, which can be a major source of disease in the city.

SPATIAL AND TEMPORAL ANALYSIS OF POLLUTANT GASES IN WESTERN BLACK SEA OF TURKIYE

Abstract. Environmental pollution, particularly air pollution, is one of the foremost problems we face today. Air pollution has become a global issue that affects not only regional areas but also the entire planet. The increase in the amount and concentration of pollutants or harmful substances in the atmosphere, such as various gases, particulate matter, and water vapor, causes air pollution. The rise in these substances can be due to human activities or natural environmental factors. It is crucial to examine air quality to reduce the harm inflicted on living and non-living entities. In this study, the spatial and temporal analysis of air pollutants (CO, NO2, UV_AER) in the Western Black Sea region was conducted using the Sentinel-5 TROPOMI satellite sent to monitor climate change and air quality. The Google Earth Engine platform was used to obtain the data. Monthly pollution maps were created for the year 2022, and the primary sources of pollutants were analysed. As a result, it was observed that pollutants changed on a monthly and seasonal basis, and areas with high pollutant concentrations in the region were identified. Mining, industrial activities, transportation networks, and domestic activities were determined to be the primary sources of air pollution in the study area.

A Hybrid Approach of Supervised Self-organizing Maps and Genetic Algorithms for Predictive Mapping of Arsenic Pollution in Groundwater Resources

A Hybrid Approach of Supervised Self-organizing Maps and Genetic Algorithms for Predictive Mapping of Arsenic Pollution in Groundwater Resources

Incorporating investigations of environmental racism into middle school science

AbstractTo promote a justice‐oriented approach to science education, we formed a research‐practice partnership between middle school science teachers, their students, curriculum designers, learning scientists, and experts in social justice to co‐design and test an environmental justice unit for middle school instruction. We examine teacher perspectives on the challenges and possibilities of integrating social justice into their standards‐aligned science teaching as they participate in co‐design and teach the unit. The unit supports students to investigate racially disparate rates of asthma in their community by examining pollution maps and historical redlining maps. We analyze interviews and co‐design artifacts from two teachers who participated in the co‐design and taught the unit in their classrooms. Our findings point to the benefits of a shared pedagogical framework and an initial unit featuring local historical content to structure co‐design. Findings also reveal that teachers can share similar goals for empowering students to use science knowledge for civic action while framing the local socio‐political factors contributing to the injustice differently, due in part to different institutional supports and constraints. Student interviews and a pre/postassessment illustrate how the unit facilitated students' progress in connecting socio‐political and science ideas to explain the impacts of particulate matter pollution and who is impacted most. Analyses illuminate how teachers' pedagogical choices may influence whether and how students discuss the impact of systemic racism in their explanations. The findings inform refinement of the unit and suggest supports needed for co‐design partnerships focused on integrating social justice and science.

Mapping of Heavy Metal Pollution in River System: A Scientometric Approach

Mapping of Heavy Metal Pollution in River System: A Scientometric Approach

Seasonal analysis and mapping of air pollution (PM10 and SO2) during Covid-19 lockdown in Kocaeli (Türkiye)

The Covid-19 epidemic has adversely affected the world in terms of health, education, economic, tourism, social and psychological. During to the epidemic, different measures were taken to prevent the epidemic, such as travel bans, curfews, stopping in production. These measures have reduced and improved air pollution. Within the scope of this study, the change in air pollution in Kocaeli between 2019 and 2021 was examined monthly. PM10 and SO2 maps were created with inverse distance weighted (IDW) technique using geographic information systems technology (GIS). The year 2020, when Covid-19 measures were taken, was compared with 2019 and 2021. Change maps were created by taking the difference between 2020-2019 and 2021-2020 with GIS technology. As a result of the research, it was determined that the level of air pollution decreased in 2020. On the contrary, in 2021, an increase in air pollution levels was observed. In the study, a decrease was observed in PM10 concentration during the Covid-19 lockdowns, however a decrease was not observed for SO2.

Does residential exposure to air pollutants influence mortality and cardiovascular morbidity of older people from primary care?

BackgroundDiseases affecting the cardiovascular system are the most common cause of death worldwide. In addition to classical risk factors of atherosclerosis, long-term exposure to particulate matter with particles of size up to 10 µm (PM10) in the atmosphere has become an increasing focus of scientific attention in recent decades. This study analyses the associations of residential-associated air pollutants exposure with all-cause mortality and cardiovascular morbidity of older patients in a primary care setting.MethodsThe “German Epidemiological Trial on Ankle Brachial Index” (getABI) is a prospective cohort study that started in 2001 and included 6,880 primary care patients with a follow-up of 7 years. The PM10 and nitrogen dioxide (NO2) concentrations in the atmosphere are interpolated values from the study "Mapping of background air pollution at a fine spatial scale across the European Union". The primary outcome in this analysis is death of any cause, a secondary outcome is onset of PAD. Cox proportional hazards regression was used in a two-step modelling, the first step with basic adjustment only for age, sex, and one or more air pollutants, the second with additional risk factors.ResultsA total of 6,819 getABI patients were included in this analysis. 1,243 of them died during the study period. The hazard ratio (HR) (1.218, 95%-confidence-interval (CI) 0.949–1.562) for the risk of death from any cause was elevated by 22% per 10 µg/m3 increase of PM10 in the fully adjusted model, although not statistically significant. Increased PM10 exposure in combination with the presence of PAD had a significantly increased risk (HR = 1.560, 95%-CI: 1.059-2.298) for this endpoint in the basic adjustment, but not in the fully adjusted model. 736 patients developed peripheral artery disease (PAD) during the course of the study. There was no association of air pollutants and the onset of PAD.ConclusionsOur analysis renders some hints for the impact of air pollutants (PM10, NO2, and proximity to major road) on mortality. Interaction of PAD with PM10 was found. There was no association of air pollutants and the onset of PAD.Trial registrationGerman Clinical Trials Register: DRKS00029733 (19/09/2022).

Reliability of smart noise pollution map

AbstractThe problem of noise pollution in Baghdad, the capital city of Iraq, is getting worse every day as a result of the increased volume of traffic. This presents a considerable risk, particularly on the main roads that connect densely populated neighborhoods such as the Al-Sadr City district with the central neighborhoods of the capital. In order to inform decision-makers in urban development and environmental policy about the high values of noise pollution that require remediation and regulation, noise maps are produced. However, two fundamental problems are generally faced in creating a more reliable noise map in the shortest possible time: the excessive time requirements for measuring noise and determining the method of map creation. Therefore, the role of geographic information system (GIS) software in producing noise maps is evident due to the difficulty of increasing the spatial density of measurements and integrating them with spatial information. Hence, an appropriate interpolation method is required. In this article, Moran’sIindex was calculated to assess the spatial autocorrelation of measured traffic noise points. A comparison was made between the Smart Map Plugin ordinary kriging (OK) and the inverse distance weighting (IDW) deterministic interpolation method to determine the best method for producing noise maps for the main entrance and exit roads of Al-Sadr City. The noise values were modeled using the best-performing method. Furthermore, the predictive raster data are displayed in the spatial context as a starting point and reference for identifying and understanding the levels of traffic noise in the selected study area. The locations of selected points for measuring traffic noise values were determined in an organized and homogeneous manner, where noise points for the main entrance and exit roads were opposite each other, and the distance between consecutive noise points on each road was 100 m. Traffic noise measurements were carried out at each selected point using the SVAN977 sound and vibration analyzer. At each measurement point, three noise values (LAeq, Max, Min) were obtained during the three peak times, 7–9 AM, 12–2 PM, and 4–6 PM. QGIS software was used to compare the two interpolation methods, with its strength lying in the use of plugins that facilitate spatial analysis, processing tools, and algorithms. The Smart Map Plugin provided facilities to choose the appropriate semi-variogram in the OK interpolation method. The root mean square error was used to compare the two interpolation methods in order to determine the most suitable method for producing traffic noise maps in the study area. The results indicated that the Smart Map Plugin using OK outperformed the IDW method, as spatial distribution pattern and homogeneity affect the accuracy of interpolation. Moreover, based on the analysis of the three noise attributes (LAeq, Max, Min), the performance of the Smart Map Plugin (OK) was found to be better than IDW when the Moran’sIvalue was high.

Automatic Monitoring of Water Pollution from Sentinal-2 Satellite Imagery A Case Study of Mediterranean Coast, Egypt

Water is the most important source among the natural sources on the earth's surface for the health of marine and near-shore ecosystems. Assessing water pollution in coastal areas is an essential process for sustainable development. El Gharbia coast, Egypt is one of the most important coasts of Egypt. The main objective of this work is to predict the future changes of water pollution in this coast using Sentinel-2 satellite imagery of three consequent times. First, three Sentinel-2 satellite imagery of consequent dates were acquired and processed for further classification process. The maximum likelihood classification algorithm was then used to prepare the base maps for: time 1, time 2 and time 3, with ten major classes (pollutants). The classified images of time 1 and time 2 were then used to predict the time 3 water pollution map using a Markov Chain Model. After that, the final predicted water pollution map for time 3 was validated with the classified one of the same time. Finally, and compared with the water pollution map of time 3, the future ratios of all types of pollutants have been predicted. The results showed that the proposed model can simulate water pollution changes with reliable results. Based on the simulated water pollution map and by 2030, the ratios of all pollutants will increase. Accordingly, El Gharbia coast and surrounding activities can be saved from more pollution in the future.

Tawny Owl Strix aluco Distribution in the Urban Landscape: The Effect of Habitat, Noise and Light Pollution

At present, the intensification of urban landcover is one of the most critical threats for biodiversity. Common side-effects of urban sprawl are anthropogenic noise and artificial light at night (ALAN). Although their negative effects have often been described, little research has concerned nocturnal wildlife, especially avian predators. Here, we investigated the effect of urban and tree cover, traffic noise and ALAN on the presence of the Tawny Owl Strix aluco, a common night-active predator in Europe. We conducted playback surveys along an urban gradient in Turin (Italy) to detect species presence. Traffic noise was measured in the field, the cover of built-up and (semi-)natural areas was estimated using GIS and multiple measures of ALAN were acquired from a light pollution map. We modelled species presence as a function of each environmental predictor and we found a significant negative relationship with light pollution, which was the foremost urban stressor affecting Tawny Owl occurrence. Our findings suggest that Tawny Owls are more likely to be found in less artificially illuminated areas and that their distribution in urban areas is not only influenced by noise pollution and the availability of suitable habitat, but also the intensity of ALAN. Therefore, light pollution could be a key driver of the spatial distribution of Tawny Owls and potentially other nocturnal species in urban ecosystems.

Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data

PurposeThe acquisition conditions of medical imaging are often precisely defined, leading to a high homogeneity among different data sets. Nonetheless, outliers or artefacts still appear and need to be reliably detected to ensure a reliable diagnosis. Thus, the algorithms need to handle small sample sizes especially, when working with domain specific imaging modalities.MethodsIn this work, we suggest a pipeline for the detection and segmentation of light pollution in near-infrared fluorescence optical imaging (NIR-FOI), based on a small sample size. NIR-FOI produces spatio-temporal data with two spatial and one temporal dimension. To calculate a two-dimensional light pollution map for the entire image stack, we combine region growing and k-nearest neighbours (kNN), which classifies pixels into fore- and background by its entire temporal component. Thus, decision-making on reduced data is omitted.ResultsWe achieved a F_1 score of 0.99 for classifying a data set as light polluted or pollution-free. Additionally, we reached a total F_1 score of 0.90 for detecting regions of interest within the polluted data sets. Finally, an average Dice’s coefficient measuring the segmentation performance over all polluted data sets of 0.80 was accomplished.ConclusionsA Dice’s coefficient of 0.80 for the area segmentation does not seem perfect. However, there are two main factors, besides true prediction errors, lowering the score: Segmentation mistakes on small areas lead to a rapid decrease in the score and labelling errors due to complex data. However, in combination with the light-polluted data set and pollution area detection, these results can be considered successful and play a key role in our general goal: Exploiting NIR-FOI for the early detection of arthritis within hand joints.

Improving the mapping accuracy of soil heavy metals through an adaptive multi-fidelity interpolation method

Soil heavy metal pollution poses a serious threat to environmental safety and human health. Accurately mapping the soil heavy metal distribution is a prerequisite for soil remediation and restoration at contaminated sites. To improve the accuracy of soil heavy metal mapping, this study proposed an error correction-based multi-fidelity technique to adaptively correct the biases of traditional interpolation methods. The inverse distance weighting (IDW) interpolation method was chosen and combined with the proposed technique to form the adaptive multi-fidelity interpolation framework (AMF-IDW). In AMF-IDW, sampled data were first divided into multiple data groups. Then one data group was used to build the low-fidelity interpolation model through IDW, while the other data groups were treated as high-fidelity data and used for adaptively correcting the low-fidelity model. The capability of AMF-IDW to map the soil heavy metal distribution was evaluated in both hypothetical and real-world scenarios. Results showed that AMF-IDW provided more accurate mapping results compared with IDW and the superiority of AMF-IDW became more evident as the number of adaptive corrections increased. Eventually, after using up all data groups, AMF-IDW improved the R2 values for mapping results of different heavy metals by 12.35–24.32%, and decreased the RMSE values by 30.35%–42.86%, indicating a much higher level of mapping accuracy relative to IDW. The proposed adaptive multi-fidelity technique can be equally combined with other interpolation methods and provide promising potential in improving the soil pollution mapping accuracy.

Data mining to aquifer vulnerability assessment

The vulnerability map of aquifer pollution is a part of the early warning system to prevent the deterioration of groundwater quality. The weighted index overlay methods are commonly used to map aquifer vulnerability, but they have a series of drawbacks, indicating the need to apply alternative methods that introduce the least number of a priori considerations in the parameters processing and allow a more accurate interpretation of the final results. The purpose of this study is to use data mining techniques for cluster analysis to evaluate the vulnerability of groundwater pollution in the Almondarez Vento Karst Basin in Havana Province, Cuba, and compare the results with those obtained using the RISK method, which is a weighted index overlay method to study karstic aquifers. The variables selected to apply this unsupervised classification technique were: aquifer lithology, topographic slope of the terrain, soil attenuation index to pollutants, fault density per km2 and presence of direct infiltration zones. The cluster analysis achieved greater spatial discrimination and definition of areas with different degrees of vulnerability, demonstrating its high resolution power.

English

Cell phones and mobile devices have been an integral part of life over the past 30 years. In parallel with the intensity of use of mobile phones, the number of base stations has also increased. The increasing number of base stations has caused an intensity of electromagnetic pollution in the GSM band. The issue of electromagnetic pollution has become increasingly remarkable due to the claims that it can cause diseases and scientific studies carried out in this field. The number and variety of studies carried out in the last 30 years in this regard has also increased. Base stations are established with various features. While some kinds of base stations use radial antenna type, some of them use directional antennas. Depending on the region where it is established, the cell types it uses may also differ. Therefore, multiple measurements are required to analyse the impact of a base station. However, it is practically not possible to make an infinite number of measurements. Within the scope of this study, it is aimed to refine and clarify the electromagnetic pollution maps by using artificial intelligence methods. As a result of the studies carried out in this area, electromagnetic pollution maps have become much more detailed. Key words: Electromagnetic pollution, GSM, base station.

Tropospheric formaldehyde levels infer ambient formaldehyde-induced brain diseases and global burden in China, 2013–2019

Although air pollutions cause human diseases, no epidemiological study has investigated the effect of exposure to air pollutants on brain diseases in the general population. Our objective was to examine the association between tropospheric airborne pollutants and human health risk and global burden, especially, attributable to indoor formaldehyde (FA) pollution in China. The data of tropospheric pollutants, such as: CO, NO, O3, PM2.5 or PM10, SO2, and FA in China, 2013–2019, which were derived from the database of satellite remote-sensing, were first calculated and then analyzed them according to satellite cloud pictures. The rate of prevalence, incidence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) of the Chinese population was obtained from the Global Burden of Diseases (GBD 2010). A linear regression analysis was used to evaluate the relationship between tropospheric FA concentrations and GBD indexes of human brain diseases, the numbers of fire plot, the average summer temperature, population density and car sales in China from 2013 to 2019. Our results showed that the levels of tropospheric FA could reflect the degree of indoor air FA pollution on a nationwide scale in China; in particular, only tropospheric FA exhibited a positive correlation with the rates of both prevalence and YLDs in brain diseases including: Alzheimer's disease (AD) and brain cancer, but not in Parkinson's disease and depression. In particular, the spatial-temporal changes in tropospheric FA levels were consistent with the geographical distribution of FA exposure-induced AD and brain cancer in both sex old adults with age (60–89). In addition, summer average temperature, car sales and population density were positively correlated with tropospheric FA levels in China, 2013–2019. Hence, mapping of tropospheric pollutants could be used for air quality monitoring and health risk assessment.

Investigating heavy-metal soil contamination state on the rate of stomach cancer using remote sensing spectral features

Heavy metal (HM) contamination in agricultural soils has been a serious environmental and health problem in the past decades. High concentration of HM threatens human health and can be a risk factor for many diseases such as stomach cancer. In order to investigate the relationship between HM content and stomach cancer, the under-study area should be adequately large so that the possible relationship between soil contamination and the patients' distribution can be studied. Examining soil content in a vast area with traditional techniques like field sampling is neither practical nor possible. However, integrating remote sensing imagery and spectrometry can provide an unexpensive and effective substitute for detecting HM in soil. To estimate the concentration of arsenic (As), chrome (Cr), lead (Pb), nickel (Ni), and iron (Fe) in agricultural soil in parts of Golestan province with Hyperion image and soil samples, spectral transformations were used to preprocess and highlight spectral features, and Spearman's correlation was calculated to select the best features for detecting each metal. The generalized regression neural network (GRNN) was trained with the chosen spectral features and metal containment, and the trained GRNN generated the pollution maps from the Hyperion image. Mean concentration of Cr, As, Fe, Ni, and Pb was estimated at 40.22, 11.8, 21,530.565, 39.86, and 0.5mg/kg, respectively. Concentrations of As and Fe were near the standard limit and overlying the pollution maps, and patients' distribution showed high concentrations of these metals can be considered as stomach cancer risk factors.

Geoinformation and remote methods for assessing snow cover pollution (Kyzyl)

The article presents the results of the use of geoinformation systems (GIS) and Earth remote sensing (ERS) in the mapping and analysis of snow pollution in urban areas. The research was carried out on the basis of the generated database. The use of geoinformation systems tools allows to create electronic maps to assess the current ecological state of the territory, and the use of ERS data helps to simplify the assessment of pollution levels. Thematic maps have been constructed in the geoinformation environment: the level of lead pollution (mg/kg) (m2 *day) by year, the density of atmospheric dust deposition on snow cover, and others. The assessment of the snow cover pollution of the ERS was carried out on the basis of images from the LANDSAT catalog of the Google Earth Engine (GEE) platform. The following indicators were calculated: NDSI — normalized difference index, S3 — normalized index, SCI — pollution index. A significant correlation of the NDSI (or S3) index with the concentrations of Mn, Pb and Zn was revealed. The spatial distribution of the values of these indicators is displayed by the kriging method using the available open modules and scripts of the GEE platform. Maps of the spatial distribution of the main chemical elements of solid snow and meltwater sediment were obtained.

Geoinformation and remote methods for assessing snow cover pollution (Kyzyl)

The article presents the results of the use of geoinformation systems (GIS) and Earth remote sensing (ERS) in the mapping and analysis of snow pollution in urban areas. The research was carried out on the basis of the generated database. The use of geoinformation systems tools allows to create electronic maps to assess the current ecological state of the territory, and the use of ERS data helps to simplify the assessment of pollution levels. Thematic maps have been constructed in the geoinformation environment: the level of lead pollution (mg/kg) (m2 *day) by year, the density of atmospheric dust deposition on snow cover, and others. The assessment of the snow cover pollution of the ERS was carried out on the basis of images from the LANDSAT catalog of the Google Earth Engine (GEE) platform. The following indicators were calculated: NDSI — normalized difference index, S3 — normalized index, SCI — pollution index. A significant correlation of the NDSI (or S3) index with the concentrations of Mn, Pb and Zn was revealed. The spatial distribution of the values of these indicators is displayed by the kriging method using the available open modules and scripts of the GEE platform. Maps of the spatial distribution of the main chemical elements of solid snow and meltwater sediment were obtained.