There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ-Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.

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<p><strong>Abstract.</strong> APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) is an international collaborative project to examine the emissions, processes and health effects of air pollution in Beijing. The four research themes of APHH-China are: (1) sources and emissions of urban atmospheric pollution; (2) processes affecting urban atmospheric pollution; (3) exposure science and impacts on health; and (4) interventions and solutions to reduce health impacts. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1–3 provide scientific data for Theme 4 on the development of cost-effective solutions. A key activity within APHH-Beijing was the two month-long intensive field campaigns at two sites: (i) central Beijing, and (ii) rural Pinggu. The coordinated campaigns provided observations of the atmospheric chemistry and physics in and around Beijing during November–December 2016 and May–June 2017. The campaigns were complemented by numerical air quality modelling and air quality and meteorology data at the 12 national monitoring stations in Beijing. This introduction paper provides an overview of (i) APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it in Beijing, and (iii) the air quality and meteorological conditions during the two field campaigns. The winter campaign was characterized by high PM<sub>2.5</sub> pollution events whereas the summer experienced high ozone pollution events. Air quality was poor during the winter campaign, but less severe than in the same period in 2015 when there were a number of major pollution episodes. PM<sub>2.5</sub> levels were relatively low during the summer period, matching the cleanest periods over the previous five years. Synoptic scale meteorological analysis suggests that the greater stagnation and weak southerly circulation in November/December 2016 may have contributed to the poor air quality.</p>

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

Low-cost sensors were deployed at five locations in a growing, semi-urban settlement in southwest Nigeria between June 8 and July 31, 2018 to measure particulate matter (PM2.5 and PM10), gaseous pollutants (CO, NO, NO 2, O3 and CO2), and meteorological variables (air temperature, relative humidity, wind speed and wind-direction). The spatial and temporal variations of measured pollutants were determined, and the probable sources of pollutants were inferred using conditional bivariate probability function (CBPF). Hourly PM2.5 and PM10 concentrations ranged from 20.7 ± 0.7 to 36.3 ± 1.6 µgm -3 and 47.5 ± 1.5 to 102.9 ± 5.6 µgm -3 , respectively. Hourly gaseous pollutant concentrations 21 ranged from 348 ± 132 to 542 ± 200 ppb CO, 21.5 ± 7.2 ppb NO2 and 57.5 ± 11.3 to 64.4 ± 14.0 ppb O 3. Kruskal-Wallis ANOVA on ranks determined statistically significant spatial differences in the hourly-average pollutant concentrations. Diel variation analyses indicated that CO2, PM2.5, and PM10 24 peaked in the early hours of most days, O 3 at noon while NO, NO2, and CO peaked in the evening. 25 Most pollutants were of anthropogenic origins and exhibited the highest contributions from the 26 southwest at most sampling locations. There were strong similarities between pollutants source 27 contribution at two of the monitoring sites that were in residential areas with a frequently used paved 28 road. Mitigation strategies need to be established to avoid further deterioration of ambient air quality that negatively affect public health.