Browsing by Author "Chacón-Mateos, Miriam"

Now showing 1 - 4 of 4

Open Access
Application of source apportionment methods to identify emission sources with the help of ambient air quality measurements in six European cities
(2019) Chacón-Mateos, Miriam
The quantification of pollution sources' contributions is a crucial element for implementing the Directives on Air Quality (2008/50/EC and 2004/107/EC). Within the ICARUS project, the source apportionment of PM2.5 in six cities of Europe (Athens, Brno, Ljubljana, Madrid, Thessaloniki, and Stuttgart) was assessed using datasets of measurements made in two different seasons (summer and winter) and two modeling tools: the Lenschow approach and the Positive Matrix Factorization. In order to increase the reliability and robustness of the results, an inter-comparison exercise was carried out with two receptor models for the same datasets: the Principal Component Analysis and the Positive Matrix Factorization model run by a different institution. It was observed that the Lenschow approach does not show the same results as the receptor models and therefore cannot be used to design strategies for urban air quality planning but just as a qualitative method. The comparison of the receptor models led to the validation of the results. Five sources have been found to be the main sources of PM2.5 in all the participating cities, namely, traffic, secondary inorganic aerosols (nitrates and sulphates), resuspension of soil dust, heating systems (biomass burning and/or fuel oil combustion) and industry. Geographical and seasonal variations have been observed, especially for heating sources hence, air quality measures at a local scale should be designed for the abatement of air pollution.
Open Access
Calibration method for particulate matter low-cost sensors used in ambient air quality monitoring and research
(2021) Venkatraman Jagatha, Janani; Klausnitzer, André; Chacón-Mateos, Miriam; Laquai, Bernd; Nieuwkoop, Evert; Mark, Peter van der; Vogt, Ulrich; Schneider, Christoph
Over the last decade, manufacturers have come forth with cost-effective sensors for measuring ambient and indoor particulate matter concentration. What these sensors make up for in cost efficiency, they lack in reliability of the measured data due to their sensitivities to temperature and relative humidity. These weaknesses are especially evident when it comes to portable or mobile measurement setups. In recent years many studies have been conducted to assess the possibilities and limitations of these sensors, however mostly restricted to stationary measurements. This study reviews the published literature until 2020 on cost-effective sensors, summarizes the recommendations of experts in the field based on their experiences, and outlines the quantile-mapping methodology to calibrate low-cost sensors in mobile applications. Compared to the commonly used linear regression method, quantile mapping retains the spatial characteristics of the measurements, although a common correction factor cannot be determined. We conclude that quantile mapping can be a useful calibration methodology for mobile measurements given a well-elaborated measurement plan assures providing the necessary data.
Open Access
Evaluation of a low-cost dryer for a low-cost optical particle counter
(2022) Chacón-Mateos, Miriam; Laquai, Bernd; Vogt, Ulrich; Stubenrauch, Cosima
The use of low-cost sensors for air quality measurements has become very popular in the last few decades. Due to the detrimental effects of particulate matter (PM) on human health, PM sensors like photometers and optical particle counters (OPCs) are widespread and have been widely investigated. The negative effects of high relative humidity (RH) and fog events in the mass concentration readings of these types of sensors are well documented. In the literature, different solutions to these problems - like correction models based on the Köhler theory or machine learning algorithms - have been applied. In this work, an air pre-conditioning method based on a low-cost thermal dryer for a low-cost OPC is presented. This study was done in two parts. The first part of the study was conducted in the laboratory to test the low-cost dryer under two different scenarios. In one scenario, the drying efficiency of the low-cost dryer was investigated in the presence of fog. In the second scenario, experiments with hygroscopic aerosols were done to determine to which extent the low-cost dryer reverts the growth of hygroscopic particles. In the second part of the study, the PM10 and PM2.5 mass concentrations of an OPC with dryer were compared with the gravimetric measurements and a continuous federal equivalent method (FEM) instrument in the field. The feasibility of using univariate linear regression (ULR) to correct the PM data of an OPC with dryer during field measurement was also evaluated. Finally, comparison measurements between an OPC with dryer, an OPC without dryer, and a FEM instrument during a real fog event are also presented. The laboratory results show that the sensor with the low-cost dryer at its inlet measured an average of 64 % and 59 % less PM2.5 concentration compared with a sensor without the low-cost dryer during the experiments with fog and with hygroscopic particles, respectively. The outcomes of the PM2.5 concentrations of the low-cost sensor with dryer in laboratory conditions reveal, however, an excess of heating compared with the FEM instrument. This excess of heating is also demonstrated in a more in-depth study on the temperature profile inside the dryer. The correction of the PM10 concentrations of the sensor with dryer during field measurements by using ULR showed a reduction of the maximum absolute error (MAE) from 4.3 µg m-3 (raw data) to 2.4 µg m-3 (after correction). The results for PM2.5 make evident an increase in the MAE after correction: from 1.9 µg m−3 in the raw data to 3.2 µg m−3. In light of these results, a low-cost thermal dryer could be a cost-effective add-on that could revert the effect of the hygroscopic growth and the fog in the PM readings. However, special care is needed when designing a low-cost dryer for a PM sensor to produce FEM similar PM readings, as high temperatures may irreversibly change the sampled air by evaporating the most volatile particulate species and thus deliver underestimated PM readings. New versions of a low-cost dryer aiming at FEM measurements should focus on maintaining the RH at the sensor inlet at 50 % and avoid reaching temperatures higher than 40 ∘C in the drying system. Finally, we believe that low-cost dryers have a very promising future for the application of sensors in citizen science, sensor networks for supplemental monitoring, and epidemiological studies.
Open Access
Feasibility study on the use of NO2 and PM2.5 sensors for exposure assessment and indoor source apportionment at fixed locations
(2024) Chacón-Mateos, Miriam; Remy, Erika; Liebers, Uta; Heimann, Frank; Witt, Christian; Vogt, Ulrich
Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies.