Laboratory and field evaluation of a low-cost optical particle sizer.

Low-cost sensors are widely used to collect high-spatial-resolution particulate matter data that traditional reference monitoring devices cannot. In addition to the mass concentration, the number concentration and size distribution are also fundamental in determining the origin and hazard level of particulate pollution. Therefore, low-cost optical sensors have been improved to establish optical particle sizers (OPSs). In this study, a low-cost OPS, the Nova SDS029, is introduced, and it is evaluated in comparison to two reference instruments-the GRIMM 11-D and the TSI 3330. We first tested the sizing accuracy using polystyrene latex spheres. Then, we assessed the mass and number size distribution accuracy in three application scenarios: indoor smoking, ambient air quality, and mobile monitoring. The evaluations suggest that the low-cost SDS029 rivals research-grade optical sizers in many aspects. For example, (1) the particle diameters obtained with the SDS029 are close to the reference instruments (usually < 10%) in the 0.3-5 µm range; (2) the number of particles and mass concentration are highly correlated (r ≥ 0.99) with the values obtained with the reference instruments; and (3) the SDS029 slightly underestimates the number concentration, but the derived PM2.5 values are closer to monitoring station than the reference instruments. The successful application of the SDS029 in multiple scenarios suggests that a plausible particle size distribution can be obtained in an easy and cost-efficient way. We believe that low-cost OPSs will increasingly be used to map the sources and risk levels of particles at the city scale.

Evaluation of City-Scale Disparities in PM2.5 Exposure Using Hyper-Localized Taxi-Based Mobile Monitoring.

Efforts have been directed to pollution control of fine particles (PM2.5) because exposure to PM2.5 could result in adverse health effects. However, PM2.5 exposure disparities persisted even with largely declined concentrations. Here, we applied taxi-based measurements to characterize hyper-localized PM2.5 exposures (30 m resolution) in Xi'an in December 2019 and July 2020. A big data set was derived from the taxi-based measurements (∼6 × 106 hourly PM2.5) and was used to evaluate the performance of existing regulatory measurements in urban and rural regions. Results from regulatory measurements tend to alleviate PM2.5 exposure disparities compared with taxi-based measurements. The taxi-based measurements reported higher population-weighted average (PWA) exposure in December 2019 (90 µg/m3) and July 2020 (27 µg/m3) compared to regulatory measurements (76 and 24 µg/m3 in December and July, respectively) with a wider range of relative disparities. Results indicate that the urban region would be overrepresented by regulatory measurements, where regulatory measurements reported that 60.0-84.7% of inhabitants were exposed to PM2.5 higher than PWA, while taxi-based ones reported a smaller portion (22.6-35.2%). Significant seasonal variability in PM2.5 exposure levels was found by taxi-based measurements but not regulatory measurements. The results highlight the need for providing complementary measurements (e.g., low-cost) for exposure assessment because the rural regions could be disproportionately exposed and overlooked by existing regulatory measurements.

Characterizing spatial variations of city-wide elevated PM10 and PM2.5 concentrations using taxi-based mobile monitoring.

The spatial distribution of elevated particulate matter (PM) concentrations represents a public health concern due to its association with adverse health effects. In this study, a city-wide spatial variability of PM (PM10 and PM2.5) concentrations in Jinan, China is evaluated using a combination of measurements from 1700 fixed sites and taxi-based mobile monitoring (300 taxis recruited). The taxi fleet provides high spatial resolution and minimizes temporal sampling uncertainties that a single mobile platform cannot address. A big dataset of PM concentrations covering three land-use domains (roadway, community and open-field) and pollution episodes is derived from the taxi-based mobile monitoring (~3 × 107 pairs of PM10 and PM2.5). The ability of taxi-based mobile monitoring to characterize location-specific concentrations is assessed. We applied an "elevation ratio" to identify the elevated PM concentrations and quantified the ratios at 30-m road segments. Higher PM concentrations occurred during haze episode with lower elevation ratios in all land-use domains compares to non-haze episode. Different characteristics (distribution and range) of the elevation ratios are shown in different land-use domains which highlight the potential local emission hotspots and could have transformative implications for environmental management, thus, contribute to the effectiveness of pollution control strategy.

The evaluation and optimization of calibration methods for low-cost particulate matter sensors: Inter-comparison between fixed and mobile methods.

With the development of the air pollution control, the low-cost sensors are widely used in air quality monitoring, while the data quality of these sensors is always the most concern for users. In this study, data from nine air monitoring stations with standard PM instruments were used as reference and compared with the data of mobile and fixed PM sensors in Jinan, the capital city of Shandong Province, China. Data quality of PM sensors was checked by the cross-comparison among standard method, fixed and mobile sensors. And the impacts of relative humidity and size distribution (PM2.5/PM10) on the performance of PM sensors were evaluated as well. To optimize the calibration method for both fixed and mobile PM sensors, a two-step model was designed, in which the RH and PM2.5/PM10 ratio were both used as input parameters. We firstly calibrated the sensors with five independent models, and then all the calibrated data were linearly fitted by the LR-final model. In comparison with standard instruments, the LR-final model increased the R2 values of the PM2.5 and PM10 measured by fixed sensors from 0.89 and 0.79 to 0.98 and 0.97, respectively. The R2 values of PM2.5 and PM10 measured by the mobile sensors both increased to 0.99 from 0.79 and 0.62. Overall, the two-step calibration model appeared to be a promising approach to solve the poor performance of low-cost sensors.