using AirQo monitors (https://www.airqo.net/) for real-time particulate matter sampling and 2- month passive sampling using Palmes-type diffusion tubes supplied by Gradko International for supplementary gaseous monitoring (NO2 and O3). Monitoring devices were installed between 2.5 to 4m high (i.e. within breathing zone) to ensure pollution levels captured is reflective of population exposure. For O3 and NO2, monitoring tubes were removed at the end of every 4 weeks and shipped to a UK based ISO 17025 accredited laboratory for pollutant analyses and results relayed over emails. Volunteers were opportunistically identified and recruited to host the monitoring devices.

Mobile sampling was conducted along predefined routes converging at a stationary monitoring location. These were largely ‘popular’ _boda-boda_ routes within the respective geographic boundaries. It is standard practice for _boda-boda_ riders to operate from within specific geographic boundaries, and rider recruitment (from the predefined areas of interest) was largely informed by this fact. All devices deployed on _boda-boda_ had inbuilt GPS modules to track and attribute monitoring data to specific locations ( **see figures 2a and 12** ). Data was then streamed in near-realtime to an online platform over a local cell-phone network.

Under mobile monitoring, riders were presented with 2 scenarios:

**Scenario 1 (Opportunistic Monitoring):** Mobile data routes were identified and highlighted to the riders but _boda_ movements were not restricted i.e. riders were free to collect data alongside their regular schedules.

**Scenario 2 (Restrictive Monitoring):** Riders were asked to spend about 30 minutes in selected spots during specific times of the day i.e. morning before 08:30, afternoon between 14:00 and 16:30, and evening between 17:00 to 21:00 (riders usually stop working after 21:00). In addition, they were asked to specifically ride through the defined routes on their way to/from the selected spots. In both cases, monitoring was done during active hours of the day to correspond with riders’ typical working hours i.e. (from around 06:00 to 21:00) and the stoppage times were chosen to correspond to the highs and lows of expected typical diurnal profile ( **figure 4** ).

Real-time PM data from active monitors (AirQo and PurpleAir tools) were automatically uploaded to the cloud platform at a frequency ranging 30-80 seconds, with variations depending on mode i.e. whether static or mobile and available power options. The resulting data sets informed the average pollutant concentrations within specific static deployment neighborhood along with spatiotemporal variations from mobile sampling.<sup>14</sup>

# **2.3.1 Meteorological Data**

Supplementary meteorological data (humidity, temperature and precipitation) were obtained from weather stations situated within the national climatic zones in line with sampled locations. These compilations included both historical data sets and measurements obtained during the pollution assessment period. Sampled precipitation compilations were superimposed on pollution plots to indicatively assess the meteorological influence on seasonal variations.

> 14 All active monitors were collocated (in a controlled environment) with a high-end factory calibrated - instrument Turnkey Osiris (https://turnkey <u>instruments.com/product/osiris/) prior to deployment to establish any</u> correction factors, whereas analysis for the passive monitors was handled by an ISO 17025 accredited laboratory.

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