Search

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to-use, low-cost, sensor-based monitors which have given a new dimension to air pollutant monitoring and have democratized the air quality monitoring process by making monitors and results directly available at the community level. This study used PurpleAir © sensors for PM2.5 assessment in California, USA. The evaluation of PM2.5 from sensors included Quality Assurance and quality control (QA/QC) procedures, assessment concerning reference-monitored PM2.5 concentrations, and the formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitors also captured the peak PM2.5 concentrations due to incidents such as forest fires. In comparison with reference-monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir’s higher PM2.5 concentrations was the inclusion of moisture or water vapor as an aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. Long-term assessment (2016–2023) revealed that R2 values were between 0.54 and 0.86 for selected collocated PurpleAir sensors and regulatory monitors for hourly PM2.5 concentrations. Past research studies that were conducted for mostly shorter periods resulted in higher R2 values between 0.80 and 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor-based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring and developing mitigation strategies.

The influence of rainfall spatial variability on hydrographs modelling at catchment outlet remains an open scientific debate. [1] have proposed rainfall variability indexes aiming at summarising the influence of rainfall spatial organisation on hydrographs features. This preliminary work was based on a large simulated database. The present article shows how the proposed indexes may be used in a real case study to discriminate rainfall events for which information on spatial rainfall organization is crucial for hydrograph modelling, and therefore to better illustrate the added value of high resolution rainfall information as input of hydrological models. The presented case study is located in the Cevennes Region in south-eastern France. The tested flow events are split into two subsets according to the values of the rainfall variability indexes. The comparison between modelled and measured hydrographs is then performed separately for each subset. The results obtained suggest that, on average, modelling results taking into account high resolution rainfall data are significantly improved for the subset for which the influence of rainfall variability is expected to be significant according to the indexes values. Although limited to a relatively small number of hydrographs, this case study can be viewed as a first confirmation of the pertinence of the rainfall variability indexes proposed in [1] to investigate the influence of rainfall spatial variability on the shape of hydrographs at catchment outlet.