Addressing challenges in design of effective air quality (AQ) monitoring network is important to ensure quality and representativeness of collected AQ data. Ideal locations for a network of fixed AQ stations are difficult to determine in Doha owing to the unavailability of existing AQ data, and uncertainties associated with the AQ site representation. This paper is intended to demonstrate the use of an emerging nano- technology (micro-sensor) and geostatistic tool to map ambient air quality for oxidising gases of NO2 and ozone. Micro-sensor is a small self-adequate device that can be easily deployed in area with a difficult access, limited infrastructures or where fixed AQ stations are unavailable. In this specific study, no AQ data from fixed stations was readily available to initiate the mapping, so it was decided to use micro-sensors to provide an estimation of the AQ baseline data within Doha and was intended as a tool to understand the locations to be targeted, in priority, for future wider AQ monitoring network. A small number of micro-sensors were selected and deployed over Doha, covering different type of locations: dense urban, residential, industrial, public parks and suburb areas. After analysis of the collected data, each location was assigned with a typical AQ profile. The use of geostatistics tool enabled us to highlight the spatial relationships between sampling points. By using appropriate interpolation algorithm (kriging, co-kriging) as well as the relevant auxiliary input data, production of AQ maps was prepared from a small number of sampling points. As the study area was limited within Doha and the microsensors specifically measured NO2/O3 concentrations, the focus areas were related to traffic emissions. Integration of geographic information related to traffic, such as road network layout and traffic density showed the influence of traffic emissions to local ambient air quality and contributed to refine the AQ maps. This study was a valuable contribution to the wider scope of air quality monitoring project undertaken by QEERI and TOTAL, in which it provided a preliminary insight to the optimized design of a complete monitoring network, in terms of actual sampling locations and number of devices.


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