Air Pollution Measurements and Land-Use Regression in Urban Sub-Saharan Africa Using Low-Cost Sensors—Possibilities and Pitfalls

A Review of Urban Planning Approaches to Reduce Air Pollution Exposures

PURPOSE OF REVIEW

With only 12% of the human population living in cities meeting the air quality standards set by the WHO guidelines, there is a critical need for coordinated strategies to meet the requirements of a healthy society. One pivotal mechanism for addressing societal expectations on air pollution and human health is to employ strategic modeling within the urban planning process. This review synthesizes research to inform coordinated strategies for a healthy society. Through strategic modeling in urban planning, we seek to uncover integrated solutions that mitigate air pollution, enhance public health, and create sustainable urban environments.

RECENT FINDINGS

Successful urban planning can help reduce air pollution by optimizing city design with regard to transportation systems. As one specific example, ventilation corridors i.e. aim to introduce natural wind into urban areas to improve thermal comfort and air quality, and they can be effective if well-designed and managed. However, physical barriers such as sound walls and vegetation must be carefully selected following design criteria with significant trade-offs that must be modeled quantitatively. These tradeoffs often involve balancing effectiveness, cost, aesthetics, and environmental impact. For instance, sound walls are highly effective at reducing noise, provide immediate impact, and are long-lasting. However, they are expensive to construct, visually unappealing, and may block views and sunlight. To address the costly issue of sound walls, a potential solution is implementing vegetation with a high leaf area index or leaf area density. This alternative is also an effective method for air pollution reduction with varying land-use potential. Ultimately, emission regulations are a key aspect of all such considerations. Given the broad range of developments, concerns, and considerations spanning city management, ventilation corridors, physical barriers, and transportation planning, this review aims to summarize the effect of a range of urban planning methods on air pollution considerations.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Understanding the connections between climate change, air pollution, and human health in Africa: Insights from a literature review

Climate change and air pollution are two interconnected global challenges that have profound impacts on human health. In Africa, a continent known for its rich biodiversity and diverse ecosystems, the adverse effects of climate change and air pollution are particularly concerning. This review study examines the implications of air pollution and climate change for human health and well-being in Africa. It explores the intersection of these two factors and their impact on various health outcomes, including cardiovascular disease, respiratory disorders, mental health, and vulnerable populations such as children and the elderly. The study highlights the disproportionate effects of air pollution on vulnerable groups and emphasizes the need for targeted interventions and policies to protect their health. Furthermore, it discusses the role of climate change in exacerbating air pollution and the potential long-term consequences for public health in Africa. The review also addresses the importance of considering temperature and precipitation changes as modifiers of the health effects of air pollution. By synthesizing existing research, this study aims to shed light on complex relationships and highlight the key findings, knowledge gaps, and potential solutions for mitigating the impacts of climate change and air pollution on human health in the region. The insights gained from this review can inform evidence-based policies and interventions to mitigate the adverse effects on human health and promote sustainable development in Africa.

A meta-analysis of particulate matter and nitrogen dioxide air quality monitoring associated with the burden of disease in sub-Saharan Africa

Exposure to air pollution is a fundamental obstacle that makes it complex to realize the Sustainable Development Goals (SDGs 3) for good health and wellbeing. It is for this reason that air pollution has been characterized as the global environmental health risk facing the current generation. The risks of air pollution on morbidity, and life expectancy are well documented. This feeds directly to the substantial body of the literature that exists regarding the burden of diseases associated with ambient air pollution. However, the bulk of this literature originates from developed countries. Whilst most of the sub-Saharan African studies extrapolate literature from developed countries to contextualize the risks of elevated air pollution exposure levels associated with the burden of disease. However, extrapolation of epidemiological evidence from developed countries is problematic given that it disregards the social vulnerability. Therefore, given this observation, it is ideal to evaluate if the monitoring executions of hazardous particulate matter and nitrogen dioxide do take into consideration the concerted necessary efforts to associate monitored air pollution exposure levels with the burden of disease. Therefore, based on this background, the current meta-analysis evaluated air quality monitoring associated with the burden of disease across sub-Saharan Africa. To this extent, the current meta-analysis strictly included peer-reviewed published journal articles from the sub-Saharan African regions to gain insight on air quality monitoring associated with the burden of disease. The collected meta-analysis data was captured and subsequently analyzed using Microsoft Excel 2019. This program facilitated the presentation of the meta-analysis data in the form of graphs and numerical techniques. Generally, the results indicate that the sub-Saharan Africa is characterized by a substantial gap in the number of regional studies that evaluate the burden of disease in relation with exposure to air quality.Implications: The work presented here is an original contribution and provides a comprehensive yet succinct overview of the monitoring associated with the burden of disease in sub-Saharan Africa. The author explores if the monitoring executions of hazardous particulate matter and nitrogen dioxide do take into considerations the concerted necessary efforts to associate monitored air pollution exposure levels with the burden of disease. The manuscript includes the most relevant and current literature in a field of study that has not received a deserving degree of research attention in recent years. This is especially true in sub-Saharan Africa, characterized by insufficient monitoring of air quality exposure concentrations.

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs

BACKGROUND

Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms.

OBJECTIVES

We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies.

METHODS

We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs.

RESULTS

Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility.

DISCUSSION

Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Ambient and indoor air pollution exposure and adverse birth outcomes in Adama, Ethiopia

Air pollution poses a threat to human health, with pregnant women and their developing fetuses being particularly vulnerable. A high dual burden of ambient and indoor air pollution exposure has been identified in Ethiopia, but studies investigating their effects on adverse birth outcomes are currently lacking. This study explores the association between ambient air pollution (NO_X and NO₂) and indoor air pollution (cooking fuel type) and fetal and neonatal death in Adama, Ethiopia. A prospective cohort of mothers and their babies was used, into which pregnant women were recruited at their first antenatal visit (n = 2085) from November 2015 to February 2018. Previously developed land-use regression models were utilized to assess ambient concentrations of NO_X and NO₂ at the residential address, whereas data on cooking fuel type was derived from questionnaires. Birth outcome data was obtained from self-reported questionnaire responses during the participant's postnatal visit or by phone if an in-person meeting was not possible. Binary logistic regression was employed to assess associations within the final study population (n = 1616) using both univariate and multivariate models; the latter of which adjusted for age, education, parity, and HIV status. Odds ratios (OR) and their corresponding 95% confidence intervals (CI) were reported. Within the cohort, 69 instances of fetal death (n = 16 miscarriages; n = 53 stillbirths) and 16 cases of neonatal death were identified. The findings suggest a tendency towards an association between ambient NO_X and NO₂ exposure during pregnancy and an increased risk of fetal death overall as well as stillbirth, specifically. However, statistical significance was not observed. Results for indoor air pollution and neonatal death were inconclusive. As limited evidence on the effects of exposure to ambient air pollution on adverse birth outcomes exists in Sub-Saharan Africa and Ethiopia, additional studies with larger study populations should be conducted.

Spring 2020 Atmospheric Aerosol Contamination over Kyiv City

Extraordinarily high aerosol contamination was observed in the atmosphere over the city of Kyiv, Ukraine, during the March–April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load was investigated using newly established AirVisual sensor mini-networks in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site were analyzed for that period. Aerosol optical depth, Ångström exponent, and the aerosol particles properties (particle size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning aerosol. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv city were produced by different burning materials and phases of vegetation fires at different times. The case of enormous PM2.5 aerosol contamination in the Kyiv city reveals the need to implement strong measures for forest fire control and prevention in the Kyiv region, especially in its northwest part, where radioactive contamination from the Chernobyl disaster is still significant.

Personal Exposure to Fine Particles (PM2.5) in Northwest Africa: Case of the Urban City of Bamako in Mali

Personal exposure to particulate matter (PM) from anthropogenic activities is a major concern in African countries, including Mali. However, knowledge of particulates is scant. This study was undertaken to characterize personal exposure to PM_2.5 microns or less in diameter (PM_2.5) in the city of Bamako in Mali. The exposure to PM_2.5, through daily activities was observed from September 2020 to February 2021. Participants wore palm-sized optical PM_2.5 sensors on their chest during their daily activities. The exposure levels in four different groups of residents were investigated in relation to their daily activities. The variation in PM_2.5 concentration was measured during different activities in different microenvironments, and the main sources of exposure were identified. The highest average 10 min concentrations were observed at home and in bedrooms, while the participants were using specific products typically used in Africa, Asia, and South America that included insecticides (IST; 999 µg/m³) and incense (ICS; 145 µg/m³), followed by traffic (216 µg/m³) and cooking (150 µg/m³). The lowest average 10 min concentrations were also observed in the same microenvironment lacking IST or ICS (≤14 µg/m³). With no use of specific products, office workers and students were the least exposed, and drivers and cooks were the most exposed. The concentrations are up to 7.5 and 3 times higher than the World Health Organization's yearly and daily recommended exposure levels, respectively, indicating the need to promptly elaborate and apply effective mitigation strategies to improve air quality and protect public health. This study highlights the importance of indoor air pollution sources related to culture and confirms previous studies on urban outdoor air pollution sources, especially in developing countries. The findings could be applied to cities other than Bamako, as similar practices and lifestyles are common in different cultures.

A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda

The application of land use regression (LUR) modeling for estimating air pollution exposure has been used only rarely in sub-Saharan Africa (SSA). This is generally due to a lack of air quality monitoring networks in the region. Low cost air quality sensors developed locally in sub-Saharan Africa presents a sustainable operating mechanism that may help generate the air monitoring data needed for exposure estimation of air pollution with LUR models. The primary objective of our study is to investigate whether a network of locally developed low-cost air quality sensors can be used in LUR modeling for accurately predicting monthly ambient fine particulate matter (PM2.5) air pollution in urban areas of central and eastern Uganda. Secondarily, we aimed to explore whether the application of machine learning (ML) can improve LUR predictions compared to ordinary least squares (OLS) regression. We used data for the entire year of 2020 from a network of 23 PM2.5 low-cost sensors located in urban municipalities of eastern and central Uganda. Between January 1, 2020 and December 31, 2020, these sensors collected highly time-resolved measurement data of PM2.5 air concentrations. We used monthly-averaged PM2.5 concentration data for LUR prediction modeling of monthly PM2.5 concentrations. We used eight different ML base-learner algorithms as well as ensemble modeling. We applied 5-fold cross validation (80% training/20% test random splits) to evaluate the models with resampling and Root mean squared error (RMSE). The relative explanatory power and accuracy of the ML algorithms were evaluated by comparing coefficient of determination (R²) and RMSE, using OLS as the reference approach. The overall average PM2.5 concentration during the study period was 52.22 μg/m³ (IQR: 38.11, 62.84 μg/m³)-well above World Health Organization PM2.5 ambient air guidelines. From the base-learner and ensemble models, RMSE and R² values ranged between 7.65 μg/m³ - 16.85 μg/m³ and 0.24-0.84, respectively. Extreme gradient boosting (xgbTree) performed best out of the base learner algorithms (R² = 0.84; RMSE = 7.65 μg/m³). Model performance from ensemble modeling with Lasso and Elastic-Net Regularized Generalized Linear Models (glmnet) did not outperform xgbTree, but prediction performance was comparable to that of xgbTree. The most important temporal and spatial predictors of monthly PM2.5 levels were monthly precipitation, percent of the population using solid fuels for cooking, distance to Lake Victoria, and greenspace (NDVI) within a 500-m buffer of air monitors. In conclusion, data from locally developed low-cost PM sensors provide evidence that they can be used for spatio-temporal prediction modeling of air pollution exposures in Uganda. Moreover, the non-parametric ML and ensemble approaches to LUR modeling clearly outperformed OLS regression algorithm for the prediction of monthly PM2.5 concentrations. Deploying low-cost air quality sensors in concert with implementation of data quality control measures, can help address the critical need for expanding and improving air quality monitoring in resource-constrained settings of sub-Saharan Africa. These low-cost sensors, in conjunction with non-parametric ML algorithms, may provide a rapid path forward for PM2.5 exposure assessment and to spur air pollution epidemiology research in the region.

Measurements of NOx and Development of Land Use Regression Models in an East-African City

Air pollution causes premature mortality and morbidity globally, but these adverse health effects occur over proportionately in low- and middle-income countries. Lack of both air pollution data and knowledge of its spatial distribution in African countries have been suggested to lead to an underestimation of health effects from air pollution. This study aims to measure nitrogen oxides (NOx), as well as nitrogen dioxide (NO2), to develop Land Use Regression (LUR) models in the city of Adama, Ethiopia. NOx and NO2 was measured at over 40 sites during six days in both the wet and dry seasons. Throughout the city, measured mean levels of NOx and NO2 were 29.0 µg/m3 and 13.1 µg/m3, respectively. The developed LUR models explained 68% of the NOx variances and 75% of the NO2. Both models included similar geographical predictor variables (related to roads, industries, and transportation administration areas) as those included in prior LUR models. The models were validated by using leave-one-out cross-validation and tested for spatial autocorrelation and multicollinearity. The performance of the models was good, and they are feasible to use to predict variance in annual average NOx and NO2 concentrations. The models developed will be used in future epidemiological and health impact assessment studies. Such studies may potentially support mitigation action and improve public health.