Measuring Air Quality for Advocacy in Africa (MA3): Feasibility and Practicality of Longitudinal Ambient PM2.5 Measurement Using Low-Cost Sensors

Ambient air pollution in urban cities in sub-Saharan Africa (SSA) is an important public health problem with models and limited monitoring data indicating high concentrations of pollutants such as fine particulate matter (PM2.5). On most global air quality index maps, however, information about ambient pollution from SSA is scarce. We evaluated the feasibility and practicality of longitudinal measurements of ambient PM2.5 using low-cost air quality sensors (Purple Air-II-SD) across thirteen locations in seven countries in SSA. Devices were used to gather data over a 30-day period with the aim of assessing the efficiency of its data recovery rate and identifying challenges experienced by users in each location. The median data recovery rate was 94% (range: 72% to 100%). The mean 24 h concentration measured across all sites was 38 µg/m3 with the highest PM2.5 period average concentration of 91 µg/m3 measured in Kampala, Uganda and lowest concentrations of 15 µg/m3 measured in Faraja, The Gambia. Kampala in Uganda and Nnewi in Nigeria recorded the longest periods with concentrations >250 µg/m3. Power outages, SD memory card issues, internet connectivity problems and device safety concerns were important challenges experienced when using Purple Air-II-SD sensors. Despite some operational challenges, this study demonstrated that it is reasonably practicable and feasible to establish a network of low-cost devices to provide data on local PM2.5 concentrations in SSA countries. Such data are crucially needed to raise public, societal and policymaker awareness about air pollution across SSA.


Summary What is the key question?
Is it practical and feasible to gather data on air quality using low-cost particle sensors deployed across sub-Saharan Africa? What is the bottom line? Air pollution is a big global health problem and low-and-middle-income countries, where the use of biomass fuel is prevalent. Ambient air pollution, especially in urban areas in Africa, is largely as a result of emissions from old and poorly maintained vehicles, high sulphur fuels, dust and fumes from industries, smoking and roadside rubbish burning in addition to biomass fuels. Why read on? Air pollution by particulate matter (PM) have documented health effects: COPD, asthma, lung cancer, heart disease etc. We need to quantify the extent of the pollution as a tool for advocacy for cleaner air.
Title: Measuring Air Quality for Advocacy in Africa: Longitudinal measurement of Ambient PM2.5 in Sub Saharan African countries Research Question: Is it practical and feasible to gather data on air quality using low-cost particle sensors deployed across sub-Saharan Africa? Objective: Overall objective is to execute an air quality measurement primary study with a large geographical coverage utilising low-cost particle sensors and reliable longitudinal designs with a view to generating ambient PM2.5 data that is truly representative quantitative measurements of ground-level air pollution in sub-Saharan Africa. Specific objectives are: 1. To assess the ambient PM2.5 pollutant level over a four-week period in the nine sub-Saharan African countries (The Gambia, Tanzania, Kenya, Uganda, Nigeria, Burkina Faso, Sudan, Cameroon & Benin Republic) using a low-cost air quality measurement device (Purple air II SD device) 2. To compare the ambient PM2.5 pollutant levels in each country with the WHO air quality standard threshold for ambient PM2.5

Rationale/Background
Exposure to ambient air pollution is increasing becoming a significant health-related environmental issue. It is estimated that 3 billion people, i.e. 40% of the world's population, are exposed daily to air pollution with a majority living in low-and-middle-income-countries (LMICs). Air pollution originates largely from incompletely combusted solid fuels used in households. In urban areas in Africa, ambient air pollution also arises from emissions from old and poorly maintained vehicles, high sulphur fuels, dust and fumes from industries, smoking and roadside rubbish burning. The overall morbidity related to air pollution is further increased by its independent effect on obstructive lung diseases.

Why measure ambient air pollutants?
Inhalable, thoracic and respirable fractions of particulate matter (PM) have documented health effects associated with them. These health effects are determined to a large extent by the concentration of particles, surface area of particles, chemical constituents of particles and the biological activity of the particles. Some of these health effects are COPD, asthma, lung cancer, heart disease, stroke, arterial thrombosis, hypertension to mention a few. Measuring ambient PM 2.5 will help identify the main sources and quantify the extent of the pollution with the aim of enacting advocacy, which will eventually lead to the establishment and reinforcement of strict air quality regulation in the sub Saharan African region. These data will assist in supporting African governments to implement evidence-based policies and programmes and could also influence behavioural change in some communities/countries i.e some countries can copy and replicate successful programmes from others.

Study Design
The study is a four-week multi-centric longitudinal air pollution monitoring to evaluate the air quality in nine countries across Africa using low cost sensors. Data on ambient PM2.5 will be collected continuously.

Participants
The Physicians participating in the African Centre for Clean Air (ACCA) facilitated air quality monitoring training will carry out the PM2.5 measurement in the following locations: • Uploads in real-time to server via wifi (or mobile phone hotspot) • Logs to SD cardso data always recoverable if wifi signal lost ✓ The device has two identical sensors and so provides the facility for internal validation.

Sampling Sequence PLEASE USE THE POWER PACK GIVEN FOR POWERING DEVICE AND THE SD CARD FOR STORING DATA.IF THERE IS ACCESS TO UNINTERRUPTED POWER AND WIFI, GET NECESSARY PERMISSION AND USE THIS.
1. Set up the sampling tool-Purple air SD sampling sensor. Installation tips are: i. The housing is designed to protect the device from elements while allowing air to flow freely past the two laser counters ii. The power supply should be mounted so that it will not be submersed in water or affected by rainfall iii. Use a 'drip loop' to prevent water from running down the wires and into the electronics iv. If possible, mount the sensor in a shady spot out of direct sun v. Baseline data about the sampling site will be collected using the questionnaire in Appendix 1. Install GPS essentials ® app from the google play store or the apple app store onto your smart phone. Stand beside where the PurpleAir sensor is set up, activate the app and click on satellite. Wait for the app to acquire the GPS of your location, then record it on your form. Scan the fully filled form and send it to bawokola@gmail.com and copy gabrielokello@gmail.com. PM2.5 concentration will be sampled as already highlighted under the sampling sequence section.

Statistical Analysis Plan
Longitudinal data from all the ACCA data measurement sites will be collated, checked for consistency and completeness, and cleaned. Trends and associations in the data will be investigated using longitudinal data methods (autoregressive timeseries models, hierarchical modelling for timeseries data). We will then calculate the probability that our PM2.5 measurements exceed the current WHO standard for ambient PM2.5, with the aim of generating data that quantifies the magnitude of air pollution in these sub Saharan African countries.

Timeline
The various phases of the research project are as in the Gantt chart below:

Strengths & Weaknesses
The strength of the study lies in the use of a low cost, high performing tool for ambient air pollution measurement. Furthermore, the potential for PM 2.5 to be measured and recorded in real-time is another strong point to allude to. The downside to the project is the length of data collection. The period of one month is too short to show time series, execute spatial-temporal analysis and reveal effects of seasonality on air pollution.

Next phase
Leveraging on our one-month experience, we will progress into a one-year (12-calender months) measurement. This will generate the largest published dataset on ambient PM2.5 globally. This, in turn, will serve as an advocacy tool for enacting laws that will lead to cleaner air for citizens of developing countries like in sub-Saharan Africa.

Equipment Maintenance Technical details
Purple air dual laser air quality sensor uses laser beams to detect the particles going past by their reflectivity, like dust shimmering in a sunbeam. The PM 2.5 and PM 10 micro-gram weights are calculated from the counts. The values are averaged every 20 seconds and graphically displayed on the purple air website. PurpleAir displays the particulate matter numbers, in both the PM 2.5 and PM 10 range. It also tracks particle counts in six sizes between 0.3um and 10.0um in diameter. PurpleAir sensors read very similar numbers to other sensors of the same type (laser counters). Some sensors use very different methods to measure particles and so we expect some disagreement with their numbers. Currently, Purple Air is seeking to know the amount of disagreement between these tools. Every care is taken to place PurpleAir monitors in locations that are representative of the air we breathe every day. They are placed in neighbourhoods, on the side of houses, a few feet above our heads. They use the homeowner's Wi-Fi and power. Local pollution like cigarette smoke, BBQ's, fireplaces and idling cars can cause spikes in the short-term graphs. Quality techniques like an initial decay test and correlation testing with new and older monitors can be used. One of the issues that faces these devices, and others, is what is called "drift". This happens with optical particle sensors because of dust that may settle inside the device and may cause an offset or drift over time. A maintenance schedule is necessary with any measuring device, but ours are relatively easy to maintain and cheap to service. PurpleAir sensors can also be used indoors. The simplest way to view our data is the PurpleAir Map(http://www.purpleair.com/map). Data is also available on MesoWest's network and others. We frequently share raw data with researchers.
Kindly list out the issues and challenges encountered while installing, using maintaining or extracting data from PurpleAir II SD sensor. Please, be as clear and as comprehensive as possible. Please, write in capital letters or type for easy readability/legibility. The emergence of low-cost air quality devices offers new opportunities to simultaneously gather spatial and temporal air quality data in near-real-time, as well as engage citizens in active environmental monitoring in sub-Saharan Africa (SSA). This subsequently provides more capabilities in the assessment of human exposure to air pollution and identifying the factors leading to pollution in the various areas in SSA.
Sensor platforms are currently available to monitor a range of air pollutants and new devices are continually being introduced (Piedrahita et al., 2014). We are therefore going through a paradigm shift in how and who is monitoring air quality (Lewis and Edwards, 2016). There has been introduction of devices that are relatively lower in cost, easier to use and less bulky than traditional equipment, and offer the prospect for citizens and communities to monitor their local air quality that may affect their health (Snyder et al., 2013).

Significance:
Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. Operating any piece of technology usually comes with some challenges. The capability to manage and overcome these challenges determines the success of projects involving the use of these tools in the long run.
Extremely accurate DS3231 real-time clock (RTC) in the Purple Air II SD incorporates a battery backup and maintains accurate time keeping when main power to the device is interrupted, however there's a possibility of shifting of time stamps on devices that are operational but not connected to wifi. Objective: