Air Pollution Mapping Research Articles

An Entropy-based Method to Evaluate the Appropriate Spatial Resolution

Abstract. Geospatial data is available at increasingly finer spatial resolutions. However, such data can also be problematic because it may result in increased financial, resource and time cost. It might also be unnecessary if the phenomenon of interest does not vary at this scale or if the scientific application does not require it. This paper explores the scale effects associated with increased spatial resolution using the entropy-based local indicator of spatial association (LISA-ELSA). Results are illustrated for two datasets a digital elevation model (30 m ASTER GDEM) and for a raster PM2.5 air pollution map (1000 m). It is shown that increasing the size of the local window for the LISA can be used to explore the effect of reducing the spatial resolution. It is possible to identify areas where the spatial association is invariant with increasing window size and which could be represented with coarser pixels.

A novel prediction framework for estimating high spatial resolution near-ground PM2.5 and O3 concentrations at street-level in urban areas

National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM2.5 and O3 concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM2.5 and O3 concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM2.5 and O3 concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R2 values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM2.5 and O3 concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM2.5 concentrations.

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring

Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring.

Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 μg/m3) and RMSE (5.36 μg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For ultrafine particles (UFP), IDW_Coral's citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.

High-resolution urban air pollution mapping.

Variation in urban air pollution arises because of complex spatial, temporal, and chemical processes, which profoundly affect population exposure, human health, and environmental justice. This Review highlights insights from two popular in situ measurement methods-mobile monitoring and dense sensor networks-that have distinct but complementary strengths in characterizing the dynamics and impacts of the multidimensional urban air quality system. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. This Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts.

The Heavy Metal Biomonitoring Study Using Transplanted Lichen, Pseudevernia furfuracea (L.) Zopf, in Nevşehir, Türkiye

The goal of this research was to measure the level of air pollution in Nevşehir and to develop an air pollution map of the city utilizing Pseudevernia furfuracea (L.) Zopf as a bioindicator. In July 2002, Lichen specimens were obtained from a clean region in the Yapraklı Mountains, Çankırı, and transplanted to four distinct sites in Nevşehir. Lichen specimens were obtained again three and six months afterwards, respectively. Inductively Coupled Plasma Spectrometry (ICP-OES) was utilized to determine heavy metal amounts (Cd, Cu, Mn, Ni, Pb, and Zn). The Dimethyl sulfoxide (DMSO) technique was applied for calculating the chlorophyll a and b concentrations. According to the findings of P. furfuracea heavy metal analyses, heavy metals contents of exposed stations were found to be in first period in range of 0.26–0.39 μg g−1, 0,014–0,028 μg g−1, 1,52–2,81 μg g−1, 0,25–0,34 μg g−1,0.44–0,60 μg g−1, and 0,15–0,24 μg g−1, in second period in range of 0,37-0,44 μg g−1,0,022-0,027 μg g−1,1,88-2,77 μg g−1 ,0,25-0,32 μg g−1 , 0,47-0,60 μg g−1 and 0,18-0,35 μg g−1 for Cu, Cd, Mn, Ni, Pb, and Zn. High levels can be attributed to traffic, autumn/winter heating, and industrial operations, particularly in the city center, caused primarily by heating operations and traffic. In accordance with heavy metal analysis outcomes, P. furfuracea performed effectively as a bioindicator and showed the presence of air pollution in Nevşehir.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment.

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Economical portable sensor based hybrid monitoring for high-resolution air pollution mapping at an urban hotspot

Economical portable sensor based hybrid monitoring for high-resolution air pollution mapping at an urban hotspot

Enhancing air pollution mapping with autonomous UAV networks for extended coverage and consistency

In the context of today's pressing air pollution challenges, accurate and consistent air pollution mapping plays a pivotal role in understanding pollutant distribution and identifying pollution sources. While current technologies, including sensors and unmanned aerial vehicles (UAVs), have begun to address this issue, the full potential of UAV-based solutions remains largely untapped. This pioneering numerical study demonstrates the effective feasibility of precise and consistent air pollution mapping in local areas that exceed the coverage capacity of a single UAV. The approach involves employing multiple UAVs, which requires rigorous mission planning encompassing various complex stages. These stages include subdividing the mapping area into manageable sub-areas, evaluating the technical capabilities of each UAV, assigning specific tasks to UAVs, and conducting individual mapping operations. By endowing UAVs with full autonomy, horizontal air pollution maps are generated across different layers within the designated area. This method's distinct advantage is its simultaneous acquisition of vertical profiles at all points within the study region, eliminating the need for additional efforts. Through strategic technical analysis, it was revealed that each UAV's mission coverage area could be expanded by over 30%, leading to more consistent air pollution mapping. Furthermore, this finding suggests a reduction of up to 25% in the total number of UAVs required for studies covering significantly larger areas.

Integrating Tehran metropolitan air pollution into the current transport system and sprawl growth: an emphasis on urban performance and accessibility

Air pollution is one of the most challenging issues for urban environment and environmental management. The goal of this study was to determine the impact of Tehran metropolitan's performance and accessibility on urban transportation and air pollution as sprawl grows. Tehran, with a population of 15.98 million people, has many environmental issues, including air pollution. Secondary data were collected from the Tehran Air Quality Control Company as well as Landsat satellite imagery (OLI). The raw data of intra-city and suburban traffic counts for spatial analysis of movements, combined with the raw data of measuring stations, were then used as a sample in the ArcGIS software environment for three selected days in 2013, 2014, and 2016. Following geometric and radiometric correction, programming methods and a multivariate regression algorithm were applied to the images, yielding results in the form of additional stations. According to the results obtained (about 3.29 m) root-mean-square error (RMSE), the Inverse Distance Weighting (IDW) model was used in air pollution maps for better assessment. The findings suggest that Tehran is not the only source of air pollution and that TMA performance and accessibility play a significant role in the amount of air pollution. Furthermore, there is a strong correlation (more than 75%) between air pollution maps and transportation flow maps on specific days (February 23, 2013, February 26, 2015, and December 28, 2016). Finally, the analysis of this situation in three circles showed that the highest volume of traffic was done in the city of Tehran (CBD), suburban area (Suburban), and suburban area (Exurban), respectively, and the pattern of distribution and spatial accumulation of pollution has also been a function of this situation.

Mapping of air pollutants within urban canyons of Alexandria city fabric

Mapping of air pollutants within urban canyons of Alexandria city fabric

Smart city air quality management through leveraging drones for precision monitoring

Air pollution stands out as one of the most critical challenges impacting public health, the overall quality of life, and the pursuit of sustainable development in urban environments. Each existing air quality monitoring method carries inherent limitations alongside its advantages. This study addresses the drawbacks of both satellite-based and fixed monitoring stations by proposing a drone-based air quality monitoring solution. The entire urban area was surveyed using a multi-rotor drone equipped with sensors capable of measuring atmospheric components, resulting in the creation of high-resolution air pollution maps. The expansive urban area, exceeding the coverage capacity of a single flight, was systematically subdivided into subregions. Distinct measurements were conducted in each subregion, and a consolidated mapping was generated by combining the individual results obtained. The drone, operating autonomously, conducted precise measurements on a citywide scale, enhancing the consistency of pollutant mapping by eliminating potential errors associated with manual control. Pollution sources were identified throughout the city, and real-time data revealed how transportation-induced air pollution affected different regions. Environmental factors like wind and pressure differences were identified as influential in the varied spread of air pollutants across different parts of the city. The study underscores the pivotal role of transportation in urban air pollution, laying the groundwork for the development of targeted pollution control strategies in urban environments. Furthermore, the research validates the potential of UAVs in advancing the efficient management of smart cities, particularly through the monitoring and analysis of air pollution stemming from transportation.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

An Assessment of the Possibility of Using Unmanned Aerial Vehicles to Identify and Map Air Pollution from Infrastructure Emissions

Sustainable development and the creation of smart, green cities requires cooperation in many scientific fields, including those related to ecology, mobility, or sustainable management, among others. Environmental protection is a particularly important element here. Atmospheric pollution, due to air movements, spreads over very large areas; therefore, air quality monitoring is crucial to ensure protection from harmful substances. One of the most severe sources of air pollution, accounting for as much as approximately 25% of total annual emissions within the EU, is road transport. Therefore, the European Union has set an ambitious target to reduce total emissions to 55% for cars and 50% for vans by 2030. In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in many scientific fields, including environmental protection and photogrammetry. The use of UAVs to identify harmful pollutants allows them to gain an advantage over conventional detection methods, due to the possibility of remote (therefore safe for humans), faster, and area-based measurements. Given the ever-expanding scale of the use of this technology, this paper presents the possibilities of using UAVs to identify and visualize (map) pollution. The examples presented in the foreign literature, as well as our own research, in imaging the altitude distribution of air pollutants; gaseous pollutants: C6H6, HCHO, SO2; and particulate matter: PM1, PM2.5, PM10 demonstrate the validity of such measures. This research was carried out in the area of one of Poland’s key A4 highways. The maps obtained allow for an area-wise and altitude-wise presentation of one of the significant air pollutants in the EU. In addition, they can be a valuable source of information for the implementation of future projects and the improvement of road infrastructure, thus contributing to the reduction of air pollution and the creation of so-called “green cities”.

Efficient air pollution mapping in extensive regions with fully autonomous unmanned aerial vehicles: A numerical perspective

It is noteworthy that comprehensive exploration of atmospheric measurements in the horizontal plane using aerial platforms, necessitating high autonomy, has not been extensively covered in the existing literature. This research presents a systematic numerical approach to effective air pollution mapping achieved through the integration of horizontal and vertical air pollution measurements conducted using a fully autonomous unmanned aerial vehicle (UAV) platform. The developed robust navigation model enables the UAV to efficiently scan the extensive measurement area, which is subdivided into smaller sub-areas using the polygonal decomposition technique, resulting in a comprehensive map of the entire region. Furthermore, technical analysis determines the optimal flight speed, leading to air pollution measurements in up to 30 % more areas and ensuring more consistent results. The simulation results illustrate the effective mapping of the entire area by aggregating air pollution measurements from sub-areas, with seamless transitions emphasizing the accuracy and consistency of the employed air pollution mapping technique. This systematic method offers numerous advantages, including rapid air pollution source identification and swift response capabilities. Moreover, this approach holds potential for various applications, such as forest fire monitoring and natural resources assessment, by equipping UAVs with additional equipment like cameras alongside atmospheric sensors.

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).

Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine

Air pollution (AP) is a significant risk factor for public health, and its impact is becoming increasingly concerning in developing countries where it is causing a growing number of health issues. It is therefore essential to map and monitor AP sources in order to facilitate local action against them. This study aims at assessing the suitability of Sentinel-5 AP products based on Google Earth Engine (GEE) to monitor air pollutants, including CO, NO2, SO2, and O3 in Arak city, Iran from 2018 to 2019. Our process involved feeding satellite images to a cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, and annually. By coding in the JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were obtained. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The employed model, which solely used Sentinel-5 AP products, was tested and assessed using ground data collected from the Environmental Organization of Central Province. Our findings revealed that annual CO, NO2, SO2, and O3 were estimated with RMSE of 0.13, 2.58, 4.62, and 2.36, respectively, for the year 2018. The annual CO, NO2, SO2, and O3 for the year 2019 were also calculated with RMSE of 0.17, 2.41, 4.31, and 4.6, respectively. The results demonstrated that seasonal AP was estimated with RMSE of 0.09, 5.39, 0.70, and 7.81 for CO, NO2, SO2, and O3, respectively, for the year 2018. Seasonal AP was also estimated with RMSE of 0.12, 4.99, 1.33, and 1.27 for CO, NO2, SO2, and O3, respectively, for the year 2019. The results of this study revealed that Sentinel-5 data combined with automated-based approaches, such as GEE, can perform better than traditional approaches (e.g., pollution measuring stations) for AP mapping and monitoring since they are capable of providing spatially distributed data that is sufficiently accurate.

Spatial Analysis of SO2, PM10, CO, NO2, and O3 Pollutants: The Case of Konya Province, Turkey

Geographical information systems are frequently used in analyses of air quality based on location and time. They are also used in the creation of pollution distribution maps to determine the parameters related to air pollutants. In this study, a spatial analysis of SO2, PM10, CO, NO2 and O3 pollutants, which cause air pollution within the borders of the municipal urban areas of Konya province, was carried out for the years 2019–2020. In this context, air pollution maps were produced using the IDW interpolation method with data obtained from the National Air Quality Monitoring Network stations, which belong to the Ministry of Environment and Urbanization, in the Konya region. The results obtained were examined with maps and graphics based on the limit values found in the Air Quality Assessment and Management Regulation published by the Ministry of Environment and Urbanization. In this context, the periods of lockdown experienced during the COVID-19 pandemic were also evaluated in terms of air pollution. From the evaluation made on the values taken from the air quality stations, it can be observed that the air pollution did not violate the national limit value much in 2019 and 2020.

Linear and Nonlinear Land Use Regression Approach for Modelling PM2.5 Concentration in Ulaanbaatar, Mongolia during Peak Hours

In recent decades, air pollution in Ulaanbaatar has become a challenge regarding the health of the citizens of Ulaanbaatar, due to coal combustion in the ger area. Households burn fuel for cooking and to warm their houses in the morning and evening. This creates a difference between daytime and nighttime air pollution levels. The accurate mapping of air pollution and assessment of exposure to air pollution have thus become important study objects for researchers. The city center is where most air quality monitoring stations are located, but they are unable to monitor every residential region, particularly the ger area, which is where most particulate matter pollution originates. Due to this circumstance, it is difficult to construct an LUR model for the entire capital city’s residential region. This study aims to map peak PM2.5 dispersion during the day using the Linear and Nonlinear Land Use Regression (LUR) model (Multi-Linear Regression Model (MLRM) and Generalized Additive Model (GAM)) for Ulaanbaatar, with monitoring station measurements and mobile device (DUST TRUK II) measurements. LUR models are frequently used to map small-scale spatial variations in element levels for various types of air pollution, based on measurements and geographical predictors. PM2.5 measurement data were collected and analyzed in the R statistical software and ArcGIS. The results showed the dispersion map MLRM R2 = 0.84, adjusted R2 = 0.83, RMSE = 53.25 µg/m3 and GAM R2 = 0.89, and adjusted R2 = 0.87, RMSE = 44 µg/m3. In order to validate the models, the LOOCV technique was run on both the MLRM and GAM. Their performance was also high, with LOOCV R2 = 0.83, RMSE = 55.6 µg/m3, MAE = 38.7 µg/m3, and GAM LOOCV R2 = 0.77, RMSE = 65.5 µg/m3, MAE = 47.7 µg/m3. From these results, the LUR model’s performance is high, especially the GAM model, which works better than MRLM.