Mobile Air Quality Monitoring Research Articles

Electric buses as an air pollution and meteorological observation network: Methodology and preliminary results

Many local agencies in the United States and other countries are tasked to install air pollution monitoring systems of highly accurate sensors that have high acquisition, operating, and maintenance costs. The need for expanded coverage of air quality measurements across Salt Lake County (SLCO), Utah is being met by mounting air quality and temperature sensors on an expanding fleet of battery electric buses (BEBs). Monitoring air quality from a mobile sensor network provides real-time insights into air pollution patterns at high temporal and spatial resolution. Mobile measurements contribute to assessing residents' exposure to air pollution, facilitating the implementation of cost-effective public health policies and highlighting disparities. The Electric Bus Air Quality Observation Project was launched in SLCO during July 2021 and has collected millions of observations to date. A BEB traveling at typical traffic speeds (~10 m s−1) can provide multiple measurements along city block lengths of up to ~200 m. With careful analysis that factors in the time response of the differing sensors, variability from block-to-block may be attributed to fine-scale factors (e.g., pollution and heat sources, tree shading and urban vegetation, etc.). Preliminary findings showcase the value of increased coverage and resolution. During an extreme heat event in July 2023, both the morning and afternoon temperature readings showed differences of over 6.5 °C (12 °F), primarily as an east-west gradient with similar gradients in ozone. We conclude that temperature and pollutant concentration readings, at fine spatial and temporal resolutions, will facilitate future health studies and equitable policy and mitigation strategies. Our study demonstrates that our partnerships established with governmental, non-profit, and transit agencies facilitate the successful transfer of research and development to operational real-time mobile air quality 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.

Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges

ABSTRACT Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%.

Using Cognitive IOT In Vehicle System for On Actual Time Monitoring of CO2 Emission with Temperature and Alcohol Detector

One of the pressing environmental challenges we face today is the phenomenon of global warming, primarily driven by the release of greenhouse gases. Among these gases, carbon dioxide stands out as a significant contributor, with its emissions leading to a warming effect on the Earth's surface. Protecting our environment requires us to address and manage these changes effectively, presenting a considerable challenge. For long-term control of carbon dioxide emissions at their source, there is a growing recognition of the need for preventive and control technologies. This paper aims to employ IoT technology, utilizing Raspberry Pi's CO2 sensitivity, to monitor emissions from various sources such as public transportation, industries, and forest fires. Continuous monitoring of carbon dioxide levels within a city will help identify areas with the highest pollution levels. Additionally, this initiative seeks to implement an intelligent system for early detection of forest fires, also known as wildfires. These uncontrolled fires in natural areas pose significant threats to both ecosystems and human resources. It's noteworthy that wildfires emit more CO2 gas than initially assumed, impacting state climate targets. Integrating these efforts with IoT enhances security and enables various services, including the deployment of a Simple Notification Service (SNS) to alert mobile users about high CO2 levels in specific areas. Furthermore, this model aims to extend its capabilities to include the detection of temperature in heated vehicle components, such as engines, using temperature detectors. Moreover, alcohol detectors will be utilized to monitor alcohol consumption by vehicle drivers, enhancing safety measures on the road. Keywords: SNS (Simple Notification Service), IoT (Internet of Things), GPS (Global Positioning System), GVG (Green Vehicle Guide), MAQUMON (Mobile Air Quality Monitoring Network).

Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment.

Air pollution monitoring using mobile ground-based measurement platforms can provide high quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller scale air quality sensors, it is important to address the need for assessing these measurements in a scalable manner. We explore collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches - a standard collocation assessment technique where the mobile platform is parked near the fixed regulatory site for a period of time and an expanded approach using measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed reference site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, Colorado, USA and in the San Francisco Bay Area, California, USA. Our one-month Denver dataset includes both parked collocation periods near the fixed regulatory sites as well as general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, as the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (year-long) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40-hour running median converges to within ±4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be used during large-scale monitoring campaigns using small sensors to identify potential measurement biases.

Analyzing Air Pollution in Urban Delhi: A Quantitative Assessment of Particulate Matter (PM) Concentrations

Air pollution poses significant challenges to urban environments, with particulate matter (PM) concentrations being a major concern due to their adverse effects on public health and the environment. This research paper aims to quantitatively assess PM concentrations in urban Delhi, focusing on spatial, temporal, and seasonal variations across diverse urban microenvironments. The research design involved mobile air quality monitoring using real-time PM sensors mounted on a vehicle, coupled with Geographic Information System (GIS) analysis to map pollution hotspots and assess correlations with urban features. The key findings of the study reveal significant spatial variability in PM concentrations, with industrial zones and busy intersections emerging as hotspots for pollution. Seasonal variations in PM levels, with elevated concentrations during winter months and lower levels during the monsoon season, underscore the influence of meteorological factors on air quality dynamics. The study also identifies correlations between PM concentrations and respiratory disease cases, highlighting the adverse health effects of air pollution. The implications of these findings extend to air quality management strategies and public health interventions, emphasizing the need for targeted measures to mitigate pollution levels and protect public health in urban environments.

Spatial patterns of PM2.5 air pollution in Jakarta: Insights from mobile monitoring

A mobile air quality monitoring study in Jakarta was undertaken in 2022 by the Clean Air Catalyst consortium, in partnership with Google Earth Outreach and Aclima, taking air pollution measurements using four specially equipped Google Street View cars. This study aims to map street-level PM2.5 pollution in a set of neighborhoods (polygons) throughout Jakarta to evaluate the variability caused by local emission sources and to observe the exposure to air pollution in areas with different income levels. To address these aims, we monitored the concentration five days a week, 8 am to 5 pm, from September to December in eight polygons, stratified to include high and low-income areas. The cars covered a distance of 7,750 km and at least 22 visits per location. PM2.5 concentrations were measured at 1-second intervals using a light-scattering sensor. This high-resolution data collection allowed for detailed monitoring and analysis of concentrations in the study area. The measurements showed temporal variability consistent with meteorological and activity patterns in Jakarta, with higher measured concentrations in the morning compared to the afternoon at most polygons. Median concentrations varied by 2x - >5x within individual polygons, and this variability was particularly evident in the northern part of Jakarta, encompassing both high-income and low-income neighborhoods, where these areas are significantly affected by traffic and industrial activities. The results demonstrate the extent to which PM2.5 concentrations and exposures within Jakarta neighborhoods depend on proximity to local air pollution sources.

Low-cost mobile air quality monitoring based on internet of things for factory area

The most significant effect of industrialization is air pollution. Air pollution monitoring has become an important requirement in affected areas. With the advancement of electronic sensor technology and the Internet of Things (IoT), there are plenty of remote ambient air quality monitoring systems have been developed by measuring the levels of major air pollutants such as Sulfur Dioxide, (SO2), Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Ozone (O3), and Particulate Matter (PM). However, other hazardous gases such as Hydrogen Sulfide (H2S), Carbon Monoxide (CO), and Ammonia (NH3) also have the potential to appear in factory areas, especially factory areas located in rural areas. Therefore, this study proposes a low-cost mobile ambient air quality monitoring system by measuring CO, H2S, and NH3 gas levels that are easy to implement. The proposed system has been tested in factory areas and rural areas where factory pollutants can be mixed with agricultural pollutants. The test results show that the proposed system can detect CO, H2S, and NH3 gas levels. The levels of CO, H2S, and NH3 gases at the test site were still at a safe level. A mobile remote monitoring scheme by utilizing thinger.io as an IoT platform also works very well.

Towards hyperlocal source identification of pollutants in cities by combining mobile measurements with atmospheric modeling

While mobile, vehicle-based sampling of air quality has provided insights into pollutant distributions within cities, previous research has been limited in using observed pollution hotspots to identify and localize unknown emission sources away from the road. Such “hyperlocal source identification” requires hotspots identified through mobile sampling to be linked to emission sources by tracing atmospheric transport upwind from observed hotspots. Here we analyze almost one year's worth of pollutant observations from two Google Street View cars driven around the Salt Lake area, with repeated sampling in select neighborhoods. We then present a method to bridge the gap in hyperlocal source identification by using a high-resolution atmospheric transport model, as part of a new Stochastic Lagrangian Monte Carlo (SLMC) method to identify emission sources based on mobile observations. Case studies with both known and unknown sources are presented to illustrate the efficacy of the SLMC method in hyperlocal source identification. With the availability of techniques like SLMC, we envision mobile air quality monitoring vehicles can serve as “roving sentinels” in identifying unknown pollution sources.

Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise

Abstract. Mobile monitoring is becoming an increasingly popular technique to assess air pollution on fine spatial scales, but methods to determine specific source contributions to measured pollutants are sorely needed. One approach is to isolate plumes from mobile monitoring time series and analyze them separately, but methods that are suitable for large mobile monitoring time series are lacking. Here we discuss a novel method used to detect and isolate plumes from an extensive mobile monitoring data set. The new method relies on density-based spatial clustering of applications with noise (DBSCAN), an unsupervised machine learning technique. The new method systematically runs DBSCAN on mobile monitoring time series by day and identifies a subset of points as anomalies for further analysis. When applied to a mobile monitoring data set collected in Houston, Texas, analyzed anomalies reveal patterns associated with different types of vehicle emission profiles. We observe spatial differences in these patterns and reveal striking disparities by census tract. These results can be used to inform stakeholders of spatial variations in emission profiles not obvious using data from stationary monitors alone.

Air Pollutant Patterns and Human Health Risk following the East Palestine, Ohio, Train Derailment.

On February 3, 2023, a train carrying numerous hazardous chemicals derailed in East Palestine, OH, spurring temporary evacuation of residents and a controlled burn of some of the hazardous cargo. Residents reported health symptoms, including headaches and respiratory, skin, and eye irritation. Initial data from U.S. Environmental Protection Agency (EPA) stationary air monitors indicated levels of potential concern for air toxics based on hazard quotient calculations. To provide complementary data, we conducted mobile air quality sampling on February 20 and 21 using proton transfer reaction-mass spectrometry. Measurements were taken at 1 s intervals along routes designed to sample both close to and farther from the derailment. Mobile air monitoring indicated that average concentrations of benzene, toluene, xylenes, and vinyl chloride were below minimal risk levels for intermediate and chronic exposures, similar to EPA stationary monitoring data. Levels of acrolein were high relative to those of other volatile organic compounds, with spatial analyses showing levels in East Palestine up to 6 times higher than the local rural background. Nontargeted analyses identified levels of additional unique compounds above background levels, some displaying spatiotemporal patterns similar to that of acrolein and others exhibiting distinct hot spots. These initial findings warrant follow-up mobile air quality monitoring to characterize longitudinal exposure and risk levels.

Development of Arduino Based Air Quality Monitoring Systems for Measuring Gas Sensor

There has been an increase in the number of hazardous gases emitted in the environment due to rapid industrialization. It is becoming very important to monitor the quality of air around so as to take adequate steps in protecting ourselves. This work proposes a mobile air quality monitoring system that is portable enough to be carried around. This system is designed to give real time update on the air quality around. The system contains an air quality sensor, which measures the air quality. The system also contains an LCD screen which displays alerts, warnings and the air quality, in parts per million (ppm), on the screen. A buzzer is also implemented in the system which is triggered when the air quality, is above a certain value. The air quality in different venues in Covenant University were obtained, analysed, and compared with one another. The gases tested in this study include CO2, ammonia, and alcohol. Another experiment was conducted to check the effect that distance has on how the sensor senses the gas. Due to the unavailability of another indoor air quality measuring device for comparison, several data from different locations were taken during the course of this project in order to make comparisons. A significant observation that was made was that increase in temperature is directly proportional to poor air quality. The values of the air quality in H213 in CST ranged from 250 ppm to 275 ppm which shows that the air quality in H213 is good and the environment is conducive. The values of the air quality CST Buttery ranged from 330 ppm to 380 ppm which shows that the air quality in the Buttery is not so good and the environment is not so conducive. When the gas was placed 0 cm to 1.2 cm away from the sensor, the air quality ranged from 1000 ppm to 4500 ppm and when the gas was placed more than 1.2 cm away from the sensor, the air quality ranged from 200 ppm to 500 ppm. This shows that the sensor is more sensitive when the gas is placed close to it.

Unreported VOC Emissions from Road Transport Including from Electric Vehicles

There are widespread policy assumptions that the phase-out of gasoline and diesel internal combustion engines will over time lead to much reduced emissions of Volatile Organic Compounds (VOCs) from road transport and related fuels. However, the use of real-world emissions measurements from a new mobile air quality monitoring station demonstrated a large underestimation of alcohol-based species in road transport emissions inventories. Scaling of industry sales statistics enabled the discrepancy to be attributed to the use of ancillary solvent products such as screenwash and deicer which are not included in internationally applied vehicle emission methodologies. A fleet average nonfuel nonexhaust VOC emission factor of 58 ± 39 mg veh-1 km-1 was calculated for the missing source, which is greater than the total of all VOCs emitted from vehicle exhausts and their associated evaporative fuel losses. These emissions are independent of the vehicle energy/propulsion system and therefore applicable to all road vehicle types including those with battery-electric powertrains. In contrast to predictions, vehicle VOC emissions may actually increase given a predicted growth in total vehicle kilometers driven in a future electrified fleet and will undergo a complete VOC respeciation due to the source change.

PLUME Dashboard: A free and open-source mobile air quality monitoring dashboard

The deployment of a mobile air quality monitoring laboratory requires advanced real-time instrument monitoring and data management software, which can be prohibitively expensive. In this work we present the PLUME Dashboard: a software package built in Python designed specifically for mobile air quality monitoring purposes. It aims to provide a free and open-source alternative to comparable commercial packages, thus reducing the barrier to entry of conducting such research. This paper outlines the development of the PLUME Dashboard and justifies the design choices that were made while also providing thorough documentation and explanation for how the software works. Functionality includes real-time data display, real-time peak identification, baseline subtraction, real-time air quality and self-sampling alerts (based on wind direction and vehicle speed), and post-processing tools such as peak identification and map merging with GPS data. The functionality of PLUME Dashboard is tested using real-world data collected in Toronto and Vancouver Canada.

Constant approximation for opportunistic sensing in mobile air quality monitoring system

Monitoring air quality plays a critical role in the sustainable development of developing regions where the air is severely polluted. Air quality monitoring systems based on static monitors often do not provide information about the area each monitor represents or represent only small areas. In addition, they have high deployment costs that reflect the efforts needed to ensure sufficient quality of measurements. Meanwhile, the mobile air quality monitoring system, such as the one in this work, shows the feasibility of solving those challenges. The system includes environmental sensors mounted on buses that move along their routes, broadening the monitoring areas. In such a system, we introduce a new optimization problem named opportunistic sensing that aims to find (1) optimal buses to place the sensors and (2) the optimal monitoring timing to maximize the number of monitored critical regions. We investigate the optimization problem in two scenarios: simplified and general bus routes. Initially, we mathematically formulate the targeted problem and prove its NP-hardness. Then, we propose a polynomial-time 12-, e−12e−1-approximation algorithm for the problem with the simplified, general routes, respectively. To show the proposed algorithms’ effectiveness, we have evaluated it on the real data of real bus routes in Hanoi, Vietnam. The evaluation results show that the former algorithm guarantees an average performance ratio of 72.68%, while the latter algorithm achieves the ratio of 63.87%. Notably, when the sensors can be on (e.g., enough energy) during the whole route, the e−12e−1-approximation algorithm achieves the approximation ratio of (1−1e). Such ratio, which is almost twice as e−12e−1, enlarges the average performance ratio to 78.42%.

The air quality index based on measurements of mobile air quality monitoring station at the waste-to-energy incineration plant PLTSa Bantargebang

Ambient air quality monitoring at waste-to-energy incineration pilot plant PLTSa Bantargebang is performed using a mobile monitoring station. The mobile monitoring station is equipped with meteorological and emission (CO, O3, NO2, PM10, PM2.5, and SO2) measurement. The monitoring was performed for 24 hour with 1 minute intervals. The emission measurement data was analyzed using Indonesian Air pollution standard index regulation (PermenLHK P.14/2020). The CO, O3, NO2, PM10, and SO2 index were in good category (1-50), while the PM2.5 index was classified as moderate (65.992). The results show that the air quality at PLTSa Bantargebang is still acceptable for human health.

Monitoring of Total Hydrocarbons and Particulate Matters in AL Nasiriya City, South of Iraq.

In the current study concentrations of some air pollutants included total hydrocarbons (THCs) and particulate matter (PM10) were estimated. Samples were collected monthly from January to December 2020 in five stations in AL- Nasiriya City. Stations 1, 2, 3, and 4 were located at industrial, commercial, and residential areas, respectively, while the fifth station was located in a rural area. The Horiba Mobile Laboratory monitored air pollutants was used in the study for the estimation, it contains a mobile ambient air quality monitoring system. The results showed a variable concentrations of THCs and PM10 among seasons and stations during the study period. The highest value was found in Station 3 for THCs, while the highest concentration for PM10 was in stations 2, 3, and 4, respectively. Moreover, the results showed variable AQI values in all stations of the study for PM10. In general, all concentrations of PM10 were above the WHO and Iraqi standards.

Monitoring of Total Hydrocarbons and Particulate Matters in AL Nasiriya City, South of Iraq.

In the current study concentrations of some air pollutants included total hydrocarbons (THCs) and particulate matter (PM10) were estimated. Samples were collected monthly from January to December 2020 in five stations in AL- Nasiriya City. Stations 1, 2, 3, and 4 were located at industrial, commercial, and residential areas, respectively, while the fifth station was located in a rural area. The Horiba Mobile Laboratory monitored air pollutants was used in the study for the estimation, it contains a mobile ambient air quality monitoring system. The results showed a variable concentrations of THCs and PM10 among seasons and stations during the study period. The highest value was found in Station 3 for THCs, while the highest concentration for PM10 was in stations 2, 3, and 4, respectively. Moreover, the results showed variable AQI values in all stations of the study for PM10. In general, all concentrations of PM10 were above the WHO and Iraqi standards.

Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg

Abstract. The portable microAeth® MA200 (MA200) is widely applied for measuring black carbon in human exposure profiling and mobile air quality monitoring. Due to it being relatively new on the market, the field lacks a refined assessment of the instrument's performance under various settings and data post-processing approaches. This study assessed the mobile real-time performance of the MA200 to determine a suitable noise reduction algorithm in an urban area, Augsburg, Germany. Noise reduction and negative value mitigation were explored via different data post-processing methods (i.e., local polynomial regression (LPR), optimized noise reduction averaging (ONA), and centred moving average (CMA)) under common sampling interval times (i.e., 5, 10, and 30 s). After noise reduction, the treated data were evaluated and compared by (1) the amount of useful information attributed to retention of microenvironmental characteristics, (2) the relative number of negative values remaining, (3) the reduction and retention of peak samples, and (4) the amount of useful signal retained after correction for local background conditions. Our results identify CMA as a useful tool for isolating the central trends of raw black carbon concentration data in real time while reducing nonsensical negative values and the occurrence and magnitudes of peak samples that affect visual assessment of the data without substantially affecting bias. Correction for local background concentrations improved the CMA treatment by bringing nuanced microenvironmental changes into view. This analysis employs a number of different post-processing methods for black carbon data, providing comparative insights for researchers looking for black carbon data smoothing approaches, specifically in a mobile monitoring framework and data collected using the microAeth® series of Aethalometer.

Spatial variation of fine particulate matter levels in Nairobi before and during the COVID-19 curfew: implications for environmental justice

The temporary decrease of fine particulate matter (PM2.5) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM2.5 concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM2.5 surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM2.5 concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM2.5 were heterogeneous over space. PM2.5 concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low-cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.