AirQo Nexus
Air-quality analytics and exploration platform
Client: AirQo
What it is
AirQo's interactive analytics platform for exploring real-time and historical air-quality data from monitoring stations across African cities. Nexus brings dashboards, maps, saved analyses, trend charts, data export, and location comparisons into one dependable workflow for researchers, city planners, and communities.
The Problem
Air quality data in Africa is sparse, fragmented, and difficult to access. Communities, researchers, and policymakers need a unified platform to visualize real-time and historical pollution data to make informed decisions about environmental health.
My Role
Built AirQo Nexus and mainly maintain it alongside the AirQo website. I work across the product experience, including the home dashboard, analysis flows, interactive maps, visualizations, saved work, data export, and the responsive interface used to explore air-quality monitoring across Africa.
Approach
Built on AirQo's platform infrastructure with real-time data ingestion from air-quality monitors. Designed the experience around clear entry points: continue saved work, compare places, analyze trends, explore locations, visualize data, and export results. Interactive mapping with station overlays, historical charts, responsive layouts for field use, Redux state management, dynamic theming per group, and PostHog analytics for product insights.
Hardest Decision
Handling the data quality challenges inherent in low-cost air quality sensors. Built visualizations that accurately represent sensor uncertainty without undermining user confidence in the data. Solutions included confidence indicators and data quality flags that communicate reliability without hiding potentially useful readings.
Status
Live at nexus.airqo.net. A maintained part of AirQo's product suite, providing real-time and historical air-quality exploration across African cities with multi-tenant theming, group-based dashboards, saved analyses, and exportable data.
What I'd Change
Would add predictive air quality forecasting using machine learning, implement community-contributed data sources, and add accessibility features for visually impaired users.