Low-cost sensors are an opportunity to improve the spatial and temporal resolution of particulate matter data. However, such sensors should be calibrated under conditions close to the final ones before any monitoring actions. The paper presents the results of a collocated comparison of four models of low-cost optical sensors with a TEOM 1400a analyser. SDS011 (Nova Fitness), ZH03A (Winsen), PMS7003 (Plantower), and OPC-N2 (Alphasense) sensors were used in this research. Three copies of each sensor model were placed in a common box to compare the sensor performance under the same measurement conditions. Monitoring of the PM_2.5 fraction was conducted for almost half a year from 21 August 2017 to 19 February 2018 in Wrocław (Poland). Reproducibility between sensor units was assessed on the basis of coefficient of variation (CV). CV values were lower than 7% in the case of SDS011 and PMS7003 sensors and equal to 20% for OPC-N2 units. CV was higher than 50% for ZH03A, mainly due to malfunctions. During the measurements, the trends of outputs from sensors were generally similar to TEOM data, but significant overestimation of PM_2.5 concentrations was observed for the sensor raw data. A high linear relationship between TEOM and sensors was noticed for 1 min, 15 min, and 1-hour averaged data for PMS7003 sensors (R2≈0.83–0.89), for SDS011 units (R2≈0.79–0.86), and for one unit of ZH03A (R2≈0.74–0.81). R2 values for daily averages were at the level 0.91–0.93 for PMS7003, 0.87–0.90 for SDS011, and 0.89 for ZH03A. OPC-N2 had only a moderate linear relationship with TEOM (R2≈0.53–0.69 for daily data and 0.43–0.61 for shorter time averages). Quite large dispersion of data and high relative errors of PM_2.5 estimation were observed for concentration ranges below 20–30 μg/m³. The impact of high relative humidity level was observed for SDS011 and OPC-N2 devices—clear overestimation of outputs was observed above 80% RH.

The article presents comparison of regression methods used to obtain calibration formulas for low-cost optical particulate matter sensors. Data for analysis were taken from 1-year collocation study of PMS7003 sensors (Plantower) with research-grade instrument TEOM 1400a. The PM_2.5 fraction was considered in this study. The results of measurements showed that PMS7003 was characterized by high reproducibility between units (coefficient of variation was lower than 10%), but the raw sensor outputs significantly overestimated PM_2.5 concentrations. Data analysis revealed that simple univariate models were sufficient to obtain a good fitting quality to TEOM data; however, the best results were achieved for raw PM₁ outputs (R² ≈ 0.81). The fitting quality was improved when multi-variable equations were examined (R² ≈ 0.84). The addition of temperature and relative humidity in the models was also beneficial (R² ≈ 0.87). Stepwise selection algorithm was used to choose the best subset of variables in the model. The results of that method were compared with “all possible regression” approach, demonstrating the convenience of stepwise regression. Data from Plantower sensor were also used for training of artificial neural network. That algorithm proved to be very effective for fitting data from one sensor (R² ≈ 0.9), but it was susceptible to deviations in the data from the other units. In general, regression analysis proved to be useful for sensor systems for ambient particulate matter measurements.

Monitoring systems are needed to obtain information about particulate matter (PM) concentrations and to make such information accessible to the public. Small, low-cost, optical sensors could be used to improve the spatial and temporal resolution of PM data. The paper presents results of collocated comparison of four low-cost PM sensors and TEOM analyser, conducted from 20-08-2017 to 24-12-2017 in Wrocław, Poland. Plantower PMS7003 and Nova Fitness SDS011 sensors proved to be the best in terms of precision and were linearly correlated with TEOM data. Alphasense OPC-N2 sensors exhibited only moderate precision and linearity. Winsen ZH03A sensors had low repeatability between units and only one copy demonstrated good operation possibilities. All tested sensors had a bias in relation to PM_2.5 concentrations obtained from TEOM.

The paper presents the results of a 1.5-year evaluation study of low- and medium-cost ozone sensors. The tests covered electrochemical sensors: SensoriC O3 3E 1 (City Technology) and semiconductor gas sensors: SM50 OZU (Aeroqual), SP3-61-00 (FIS) and MQ131 (Winsen). Three copies of each sensor were enclosed in a measurement box and placed near the reference analyser (MLU 400). In the case of SensoriC O3 3E 1 sensors, the R² values for the 1-h data were above 0.90 for the first 9 months of deployment, but a performance deterioration was observed in the subsequent months (R² ≈ 0.6), due to sensor ageing processes. High linear relationships were observed for the SM50 devices (R² > 0.94), but some periodic data offsets were reported, making regular checking and recalibration necessary. Power-law functions were used in the case of SP3-61-00 (R² = 0.6–0.7) and MQ131 (R² = 0.4–0.7). Improvements in the fittings were observed for models that included temperature and relative humidity data. In the case of SP3-61-00, the R² values increased to above 0.82, while for MQ131 they increased to above 0.86. The study also showed that the measurement uncertainty of tested sensors meets the EU Directive 2008/50/EC requirements for indicative measurements and, in some cases, even for fixed measurements.

Hydrocarbons from BTEX group are dangerous pollutants that can cause a threat to environment and human health. In many cases, their presence in an ecosystem is the result of accidents and petroleum products spills. For this reason, novel, more efficient sorbents are needed for immobilization of such pollutants. This paper presents an experimental study of recycled tires polymer fibers (RTPF) used as a low-cost sorbent for BTEX compounds in soils treated with cement. RTPF are a type of waste that contains polymer fibers with high content of rubber particles. The high porosity and large specific surface area of rubber result in possibility of use of RTPF as a removal material. Effectiveness of the application of RTPF as a BTEX removal material was evaluated on a basis of concentrations values above the surface of reacting mixture (at 25 ± 3 °C). It was found that RTPF addition increased BTEX immobilization efficacy, compared to the use of Portland cement alone. The maximum percentage reduction in BTEX compounds concentration was 33% for toluene. Lower level of removal was observed for ethylbenzene (21%), and substantially, small efficacy was noted for xylenes (8%) and benzene (7%). Additionally, in order to evaluate mechanical properties of soil–cement composites, analysis of their compressive strength after 28 days of maturation was performed. Tests showed a decrease in the compressive strength of soil–cement monoliths containing RTPF.