Hawa Dawa’s approach was built on the premise that addressing the negative impacts of air pollution on public health and the environment requires ambient air quality data and data-driven insights to be seamlessly integrated into decision-making processes in matters of public health, transportation, freight and logistics management, city planning, energy consumption and more. The journey to clean air for all requires comprehensive information and data-based actions.

Prerequisites are:

  • Air Quality Networks with reliable data (quantifiable errors) to achieve increased spatial and temporal accuracy traceable to accepted standards (i.e. Directive 2008/50/EC).

  • An ecosystem of partners that allow straightforward handling of sensor network covering everything from setup, operation and gaining insights, so that less time is spent on technology handling and more time on, policy implementation and data-based behavioural recommendations.

These are the fundamentals that inspire Hawa Dawa’s product design philosophy down to the smallest details of its features. Hawa Dawa offers an all-in-one air quality platform consisting of:
Hawa Dawa Sentience measuring device

  • Collection of environmental data based on state-of-the-art IoT sensor technology
  • Regulatory-grade, TÜV-proven accuracy of the measurement result
  • Remote and real-time data availability 24/7
  • High spatial and temporal resolution of data
  • Reliable data delivery and quality
  • Highly automated and fully managed network
  • All relevant data on your screen
  • Scalable set of features
  • Easy and clear management of air quality data from various sources
  • Convenient integration of addtional data points
  • Insights into interdependencies and key drivers
  • Effective and pro-active planning and implementation of air quality measures
Traffic correlation map of a city
Programming codes
  • Added value to your application users by integrating current, historic or predictive air quality information
  • Comprehensive data for all main pollutants created from a variety of input sources (satellite, IoT sensors, public measurements)
  • Smart API functions for implementation of interactive use-cases
  • State-of-the-art technology, containerized and secure backend
  • Continuous enhancement of data quality and coverage through AI
  • Understanding of trends, influences and changes of air quality
  • Transparency and enabling action
  • Various options available with regard to level of detail, reporting period, information and presentation format
  • Generation of a higher density informational picture by integrating other available data sources on air quality
  • Data correlation analytics for analysing root causes behind specific environmental situations and pollutant levels of certain data areas over time
Hawa Dawa threshold report