Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network

Sserunjogi, Richard; Ssematimba, Joel; Okure, Deo; Ogenrwot, Daniel; Adong, Priscilla; Muyama, Lillian; Nsimbe, Noah; Bbaale, Martin; Bainomugisha, Engineer

doi:10.1016/j.dib.2022.108512

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…Model resolutions could also be a potential reason for the underestimation. Over Kampala, high spatial variability in PM 2.5 over the urban environment can contribute to model bias (Atuhaire et al, 2022), as also shown by the AirQo low-cost air quality monitors (Sserunjogi et al, 2022;Okure et al, 2022).…”

Section: East Africamentioning

confidence: 83%

Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa

Tang,

Emmons,

Worden

et al. 2023

Geosci. Model Dev.

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Abstract. The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in the East African region (5∘ S–5∘ N, 30–45∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources.

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Section: East Africamentioning

confidence: 83%

Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa

Tang,

Emmons,

Worden

et al. 2023

Geosci. Model Dev.

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“…Therefore, cost-effective high-resolution sensor networks monitoring pollution at fine spatial and temporal resolutions [7] are essential to regulating and performing smart management and making decisions in Smart Cities [8]. The importance of hyperlocal air quality sensors is that an extensive network of several compliant reference stations and a much larger number of hyperlocal sensors can deliver reliable, high temporal-resolution data at neighbourhood scales [9]. In this context, the use of IoT architectures and Smart Cities strategies allows for integrating the sensors into a bigger ecosystem and interacting with other components and services that improve its performance [10].…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of a Dryer System for IoT Hyperlocal Particulate Matter Monitoring Device

Illueca Fernández,

Martínez,

Breis

et al. 2024

IEEE Sensors J.

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Particulate matter monitoring and climate change mitigation actions have been promoted due to the Paris Agreement because of their impact on health and high mortality rate. Highresolution networks based on hyperlocal IoT sensors can play a fundamental role in improving data quality. In this context, hyperlocal refers to air quality IoT systems that allow collecting data in real-time and in the cheapest way in comparison with local reference stations. Despite these methods are powerful and widely used by the scientific community, the signal is highly affected by relative humidity.In this paper, we present a system for measuring nanoparticles based on drying the air sampled and avoiding the hygroscopic growing of the particles. To the best of our knowledge, this is the first dryer system approach developed for IoT hyperlocal sensors. In addition, the relevance of our solution is supported by the following points: i) we propose a new dryer system that has been patented; ii) our solution can be integrated into an IoT infrastructure that allows it to interact with other services and iii) our solution has been validated in a real scenario in the city of Madrid. We have observed that the integration of a dryer system improves the performance of the OPC-N3 sensor and that we can measure the PM10 and PM2.5 fractions with high precision, R 2 = 0.83. In addition, our solution can measure small particles such as PM1 with a good correlation against the reference air quality stations. Thus, our work contributes by improving high-spatial-resolution nanoparticle monitoring in correlation to official measurements to mitigate climate change.

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Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods

Jin,

Gallagher,

et al. 2024

Environ Monit Assess

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Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network

Cited by 7 publications

References 3 publications

Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa

Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa

Design and Evaluation of a Dryer System for IoT Hyperlocal Particulate Matter Monitoring Device

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods

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