Air pollution is prevalent in cities and urban centers in developing countries including sub-Saharan Africa, but ground monitoring data on local pollution remain inadequate, hindering effective mitigation. We employed low-cost sensing and measurement technologies to quantify pollution levels based on particulate matter (PM 2.5 ), NO 2 , and O 3 over a 6 month period for selected urban centers in three of the four macroregions in Uganda. PM 2.5 diurnal profiles exhibited consistent patterns across all monitoring locations with higher pollution levels manifesting from 18:00 to 00:00 and from 06:00 to 09:00; while the periods from 00:00 to 05:00 and from 09:00 to 17:00 had the lowest levels. Daily PM 2.5 varied widely between 34 and 107 μg/m 3 over a 7 day period, well within unhealthy levels (55.5−150.4 μg/m 3 ) for short-term exposure. The inconsistent daily trend are instructive for multiple pollutant assessment to aid specific policy initiatives. The results also show inverse relations between seasonal particulate levels and precipitation, that is, R (correlation coefficient) = −0.93 and −0.62 for Kampala and Wakiso, R = −0.49 and −0.44 for the Eastern region, and R = −0.65 and −0.96 for the Western region. NO 2 monthly concentrations replicated PM 2.5 spatial levels, whereas O 3 exhibited inverse relations probably due to a higher retention time in lessurbanized environments. Both PM 2.5 and NO 2 correlated positively with the resident population. Our findings show significant spatiotemporal variations and exceedances of health guidelines by about 4−6 times across most study locations (with two exceptions) for longer-term exposure. This paper demonstrably highlights the practicability and potential of low-cost approaches for air quality monitoring, with strong prospects for citizen science. This paper also provides novel information regarding air pollution that is needed to improve control strategies for reducing exposures.

No abstract

SUMMARYThe iPhone SDK provides a powerful platform for the development of applications that make use of iPhone capabilities such as sensors, GPS, Wi-Fi or Bluetooth connectivity. Thus far we observe that the development of iPhone applications is mostly restricted to using Objective-C. However, developing applications in plain Objective-C on the iPhone OS suffers from limitations such as the need for explicit memory management and lack of syntactic extension mechanism. Moreover, when developing distributed applications in Objective-C, programmers have to manually deal with distribution concerns such as service discovery, remote communication, and failure handling. In this paper, we discuss our experience on porting the Scheme programming language to the iPhone OS and how it can be used together with Objective-C to develop iPhone applications. To support the interaction between Scheme programs and the underlying iPhone APIs, we have implemented a language symbiosis layer that enables programmers to access the iPhone SDK libraries from Scheme. In addition, we have designed high-level distribution constructs to ease the development of distributed iPhone applications in an event-driven style. We validate and discuss these constructs with a series of examples including an iPod controller, a maps application and a distributed multiplayer Scrabble-like game. We discuss the lessons learned from this experience for other programming language ports to mobile platforms. key words: iPhone development; Scheme; Objective-C; language symbiosis; interactive scripting environment; event-driven programming

We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15th February 2020 to 10th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients ($$R$$ R ), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (− 0.55; 95% CI =  − 1.01– − 0.10), parks (0.29; 95% CI = 0.03–0.54), transit stations (0.29; 95% CI = 0.16–0.42), work (− 0.25; 95% CI =  − 0.43– − 0.08), and residential places (− 1.02; 95% CI =  − 1.4– − 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (− 0.99; 95% CI =  − 1.34– − 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals’ privacy.

Low-cost air quality monitoring networks can potentially increase the availability of high-resolution monitoring to inform analytic and evidence-informed approaches to better manage air quality. This is particularly relevant in low and middle-income settings where access to traditional reference-grade monitoring networks remains a challenge. However, low-cost air quality sensors are impacted by ambient conditions which could lead to over-or underestimation of pollution concentrations and thus require field calibration to improve their accuracy and reliability. In this paper, we demonstrate the feasibility of using machine learning methods for large-scale calibration of AirQo sensors, lowcost PM sensors custom-designed for and deployed in Sub-Saharan urban settings. The performance of various machine learning methods is assessed by comparing model corrected PM using k-nearest neighbours, support vector regression, multivariate linear regression, ridge regression, lasso regression, elastic net regression, XGBoost, multilayer perceptron, random forest and gradient boosting with collocated reference PM concentrations from a Beta Attenuation Monitor (BAM). To this end, random forest and lasso regression models were superior for PM 2.5 and PM 10 calibration, respectively. Employing the random forest model decreased RMSE of raw data from 18.6 μg/m 3 to 7.2 μg/m 3 with an average BAM PM 2.5 concentration of 37.8 μg/m 3 while the lasso regression model decreased RMSE from 13.4 μg/m 3 to 7.9 μg/m 3 with an average BAM PM 10 concentration of 51.1 μg/m 3 . We validate our models through cross-unit and cross-site validation, allowing analysis of AirQo devices' consistency. The resulting calibration models were deployed to the entire large-scale air quality monitoring network consisting of over 120 AirQo devices, which demonstrates the use of machine learning systems to address practical challenges in a developing world setting.