Browsing by Author "Mead, Iq"

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    Investigating the applicability of low-cost sensors for ground-based air quality monitoring networks in developing countries: a Ghana case study.
    (Cranfield University, 2020-04) Gameli Hodoli, Collins; Mead, Iq; Coulon, Frederic
    While several studies have reported on the utility of low-cost sensors for air quality campaigns in advanced countries including the development of data correction and quality improvement mechanisms thereby using them to complement regulatory monitors, there is, in contrast, limited information on the use of low-cost sensors for air pollution applications in Ghana and wider parts of Sub-Saharan Africa. This PhD study presented a proof of concept approach on the feasibility of factory calibrated Alphasense OPC-N2 for two main purposes. Firstly, the suitability of low-cost sensors for high-density ground-based air pollution studies and the applicability of the high-resolution data for quantification of atmospheric emissions. Pearson’s correlation analysis was applied to establish the reproducibility of the selected sensors for high-density ground-based air quality monitoring specifically for PM species due to the spatial and temporal variability and suitability of PM for developing urban air quality standards. Trend analysis, calendar plots and sectorial plots in the components of wind were experimented using the high-resolution data to quantify particulate matter (PM) and its sources. Hourly averaged data from the selected sensors have demonstrated the reproducibility of low-cost OPC-N2 for use in the selected environments for PM with correlation coefficients (Pearson’s, R) between 0.97 and 0.98 for PM₁ , PM₂.₅ and PM₁₀. For quantification of the species monitored, PM₁ 0 values were 500 µg/mᶟ; PM₂.₅ were a little below 90 µg/mᶟ and PM₁ values were a little below 60 µg/mᶟ. These levels though preliminary, agree with PM pollution reported from these types of environments. It was also found that PM pollution was locally characterised with low wind speed (≤ 2 ms⁻¹) tied to background activities and the surrounding environment which includes traffic, wind-blown dust and roadside food cooking and vending activities. The statistical difference in mean values (t-values of 17.3, 11.4 and 4.2 for PM₁ , PM₂.₅ and PM₁₀ respectively) of the reported PM species have shown that the sensors are better suited for PM₁₀ monitoring. Findings from this study provide a benchmark for future (AQ) studies in Ghana, particularly in the selected exemplar urban areas. It demonstrates the feasibility of the current generation of relatively low-cost PM sensors for a high-density ground-based air quality monitoring in environments typical of large parts of West and Sub Saharan Africa.
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    Overview of Performance of Selected Low-Cost Atmospheric Sensor Nodes in Ghanaian urban areas.
    (Cranfield University, 2020-01-24 09:52) gameli Hodoli, Collins; Coulon, Frederic; Mead, Iq
    The attached dataset is specific to the overview of performance of selected low-cost atmospheric sensor nodes in Ghanaian urban areas.
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    Road traffic emission dispersion modelling: an application to Hanoi and Ho Chi Minh city using ADMS.
    (Cranfield University, 2020-08) Ngo, Khoi Quang (Lucas); Mead, Iq; Harris, Neil R. P.
    Urban air quality in Vietnam has become a pressing matter that require immediate attention to ensure a sustainable development. However due to the overreliance on in-situ observations, which only measure the end result, there is limited understanding of the connection between pollution sources and concentrations. This in turn hinders the effectiveness of environmental law enforcement and management. Since road traffic is widely regarded as the main polluter, attempts have been made to adopt atmospheric dispersion models to traffic emission in Vietnam. Most however, suggest that due to input data scarcity, model applications are limited. This work therefore employed ADMS, an advanced dispersion model that is highly adaptable to produce a full mapping of road traffic derived emission for Hanoi and HCMC, i.e. Vietnam’s 2 most populated cities. Also, a modelling framework, which exploits existing, quality traffic data to generate suitable model inputs, was developed. With this framework, a detailed GIS-based road network dataset that contains road parameters, vehicle count and travel-condition-depending emission factor was produced. Carbon Monoxide was modelled as a pilot pollutant species. Resulted concentrations show an overall moderate positive correlation with observations (r = 0.4). Inadequate information on background pollution however prevents in-depth model validation to be conducted. In overall, this work demonstrates the compatibility of ADMS with the circumstance of Vietnam. Combined with an improved data processing framework, applications of dispersion model in developing countries can be greatly expanded.
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    Urban air quality management at low cost using micro air sensors: a case study from Accra, Ghana
    (American Chemical Society , 2024-11-06) Hodoli, Collins Gameli; Mead, Iq; Coulon, Frederic; Ivey, Cesunica E.; Tawiah, Victoria Owusu; Raheja, Garima; Nimo, James; Hughes, Allison; Haug, Achim; Krause, Anika; Amoah, Selina; Sunu, Maxwell; Nyante, John K.; Tetteh, Esi Nerquaye; Riffault, Véronique; Malings, Carl
    Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to nonexistent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors, which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was colocated at the Physics Department, University of Ghana, with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of these data in identification of local sources and their behavior over time. For this study, 15 weeks of data at hourly resolution with approximately 2500 data pairs were generated and analyzed (June 1, 2023, to September 15, 2023). For this time period a coefficient of determination (r2) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μg m–3 between the pre local calibration micro air sensor (i.e., out of the box) and the reference-grade instrument. Following currently accepted best practice methods (see, e.g., PAS4023) a domain specific (i.e., local) calibration factor was generated using a multilinear regression model, and when this factor is applied to the micro air sensor data, a reduction, i.e. improvement, in MAE to 1.43 μg m–3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots, to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of the box, domain calibrated, and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyperlocalized sources associated with urban areas, such as those found in major African cities.