Found 9763 publications. Showing page 211 of 391:
2015
Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning
Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021–2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM2.5) in the garden of one participant’s house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 μg m–3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model’s RMSE, MAE, and bias by 44–56, 38–48, and 41–52%, respectively.
2023
2013
2017
Poland continues to rely heavily on coal and fossil fuels for household heating, despite efforts to reduce Particulate Matter (PM) levels. The availability of reliable air quality data is essential for policymakers, environmentalists, and citizens to advocate for cleaner energy sources. However, Polish air quality monitoring is challenging due to the limited coverage of reference stations and outdated equipment. Here, we report the results of a study on the spatio-temporal variability of Particulate Matter in Legionowo, Poland, using residents’ network of low-cost sensors. Along with identifying the hotspots of household-emitted PM, (1) we propose a data quality assurance scheme for PM sensors, (2) suggest an approach for estimating the Relative Humidity-induced uncertainty in the sensors without co-location with reference instruments, and (3) develop an interpretable Machine Learning (ML) model, a Generalized Additive Model (RMSE = 6.16 μg m−3, and R2 = 0.88), for unveiling the underlying relations between PM2.5 levels and other environmental parameters. The results in Legionowo suggest that as air temperature and wind speed increase by 1 °C and 1 km h−1, PM2.5 would respectively decrease by 0.26 μg m−3 and 0.14 μg m−3 while PM2.5 increases by 0.03 μg m−3 as RH increases by 1%.
Elsevier
2023
Denne rapporten går igjennom status innen mikrosensorteknologien. Den vurderer tilgjengelighet av produkter på markedet, målte parametere og datakvalitet, samt hvilke muligheter sensorene åpner for når de blir brukt riktig.
NILU
2021
2011
Low-Processing Data Enrichment and Calibration for PM2.5 Low-Cost Sensors
Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.
2023
2022
2016