Deployment of a mobile platform to characterize spatial and temporal variation of on-road fine particles in an urban area.

Traffic-related air pollutants (TRAPs) pose a serious health hazard for residents and commuters in urban areas. In this study, a real-time mobile monitoring system was deployed in Taipei, a typical East Asian city with an overlap of high population density, traffic, and special structures (e.g., viaducts), to capture the on-road TRAPs at different times of the day. In general, black carbon, ultrafine particles (UFPs), CO concentrations, and lung deposition surface area (LDSA) were positively correlated with traffic flow, and for PM2.5, a more independent fluctuating concentration was observed. During rush-hour periods, the mean concentrations of UFPs, PM2.5, and LDSA were 6.12 × 104 ± 3.83 × 104 cm-3, 23 ± 8 µg/m3, and 2.29 × 102 ± 1.20 × 102 µm2/cm3, respectively. Additionally, the UFP number concentration and LDSA were two times higher along the high-traffic commuting route than along the lower traffic route. Pollutants tended to accumulate at sites near viaducts and high buildings and were significantly influenced by vehicle composition. In this study, the ratio of LDSA to total particle surface area concentration was used as an indicator of the degree of particle irregularity, which was directly related to aging during transport.

Household solid waste combustion with wood increases particulate trace metal and lung deposited surface area emissions.

In households, municipal solid waste (MSW) is often burned along with wood to get rid of waste, to help in ignition or simply to reduce fuel costs. The aim of this study was to characterize the influence of household waste combustion, along with wood, on the physical and chemical properties of particulate emissions in a flue gas of a masonry heater. The MSW burning alongside wood increased average particulate matter (PM) mass (65%), lung deposited surface areas (LDSA, 15%), black carbon (BC, 65%) concentrations and the average particle size in the flue gas. The influence of MSW was smaller during ignition and burning phases, but especially during fuel additions, the mass, number, and LDSA concentrations increased significantly and their size distributions moved towards larger particles. For wood burning the trace metal emissions were relatively low, but significant increase (3.3-179 -fold increase over cycle) was seen when MSW was burned along the wood. High ratios were observed especially during fuel addition phases but, depending on compounds, also during ignition and burning end phases. The highest ratios were observed for chloride compounds (HCl, KCl, NaCl). The observed increase in light-absorbing particle, trace metal and BC concentrations in flue gas when adding wood with MSW are likely to have negative impacts on air quality, visibility, human health and climate. Furthermore, metals may also affect the condition and lifetime of the burning device due to corrosion.

Variability of aerosol mass and number concentrations during taconite mining operations.

This study characterized concentration metrics of airborne nanoparticles and their time series across major operations of a taconite mine through monitoring respirable and ultrafine particle concentrations at four major processing departments of the mine: crushing, dry milling, wet milling, and pelletizing (United Taconite Mine, Iron Junction, MN, USA). We used three area stations of direct-reading instruments to estimate concentration metrics including PM1 (particles with an aerodynamic diameter <1 µm), respirable dust (particles sampled according to the respirable convention with a 50% sampling efficiency at an aerodynamic diameter of 4 µm), PN (total number concentration of particles), and lung-deposited surface area concentrations (LDSA) of particles smaller than 300 nm, on two different days. Results for each station were compared using bivariate correlation analysis to obtain insight into the spatial distribution, and intra-class correlation coefficients (ICCs) to evaluate the between-day repeatability between the measurements. Comparability of the LDSA concentrations measured by two different devices was also investigated using linear regression. Results revealed that the pelletizing operation produced the highest average LDSA concentration on both days (with a maximum concentration of 199 ± 48 µm2/cm3 in pelletizing, 141 ± 52 µm2/cm3 in crushing, 91 ± 9 µm2/cm3 in dry milling, and 85 ± 7 µm2/cm3 in wet milling). Concentrations in all operations showed a fair to excellent between-day repeatability but they were significantly different within stations of each operation. Measured LDSA concentrations did not show a linear correlation between different instruments, except for crushing.

Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions.

BACKGROUND: Land Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models. METHODS: Air pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data. RESULTS: Model explained variance (R(2)) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2) = 0.53) and non-alpine (R (2) = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors. CONCLUSIONS: LUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study-area specific models was preferable over applying combined-area alpine/non-alpine models. Correlations between pollutants were higher in the model predictions than in the measurements, so it will remain challenging to disentangle their health effects.

Harnessing AI to unmask Copenhagen's invisible air pollutants: A study on three ultrafine particle metrics.

Ultrafine particles (UFPs) are airborne particles with a diameter of less than 100 nm. They are emitted from various sources, such as traffic, combustion, and industrial processes, and can have adverse effects on human health. Long-term mean ambient average particle size (APS) in the UFP range varies over space within cities, with locations near UFP sources having typically smaller APS. Spatial models for lung deposited surface area (LDSA) within urban areas are limited and currently there is no model for APS in any European city. We collected particle number concentration (PNC), LDSA, and APS data over one-year monitoring campaign from May 2021 to May 2022 across 27 locations and estimated annual mean in Copenhagen, Denmark, and obtained additionally annual mean PNC data from 6 state-owned continuous monitors. We developed 94 predictor variables, and machine learning models (random forest and bagged tree) were developed for PNC, LDSA, and APS. The annual mean PNC, LDSA, and APS were, respectively, 5523 pt/cm3, 12.0 µm2/cm3, and 46.1 nm. The final R2 values by random forest (RF) model were 0.93 for PNC, 0.88 for LDSA, and 0.85 for APS. The 10-fold, repeated 10-times cross-validation R2 values were 0.65, 0.67, and 0.60 for PNC, LDSA, and APS, respectively. The root mean square error for final RF models were 296 pt/cm3, 0.48 µm2/cm3, and 1.60 nm for PNC, LDSA, and APS, respectively. Traffic-related variables, such as length of major roads within buffers 100-150 m and distance to streets with various speed limits were amongst the highly-ranked predictors for our models. Overall, our ML models achieved high R2 values and low errors, providing insights into UFP exposure in a European city where average PNC is quite low. These hyperlocal predictions can be used to study health effects of UFPs in the Danish Capital.

Spatiotemporal Heterogeneity of Lung-Deposited Surface Area in Zurich Switzerland: Lung-Deposited Surface Area as a New Routine Metric for Ambient Particle Monitoring.

Objective: To assess the spatiotemporal heterogeneity of lung-deposited particle surface area concentration (LDSA), while testing the long-term performance of a prototype of low-cost-low-maintenance LDSA sensors. One factor hampering epidemiological studies on fine to ultrafine particles (F-to-UFP) exposure is exposure error due to their high spatiotemporal heterogeneity, not reflected in particle mass. Though LDSA shows consistent associations between F-to-UFP exposure and health effects, LDSA data are limited. Methods: We measured LDSA in a network of ten sensors, including urban, suburban, and rural environments in Zurich, Switzerland. With traffic counts, traffic co-pollutant concentrations, and meteorological parameters, we assessed the drivers of the LDSA observations. Results: LDSA reflected the high spatiotemporal heterogeneity of F-to-UFP. With micrometeorological influences, local sources like road traffic, restaurants, air traffic, and residential combustion drove LDSA. The temporal pattern of LDSA reflected that of the local sources. Conclusion: LDSA may be a viable metric for inexpensively characterizing F-to-UFP exposure. The tested devices generated sound data and may significantly contribute to filling the LDSA exposure data gap, providing grounds for more statistically significant epidemiological studies and regulation of F-to-UFP.

Diffusion charging measurements on exhaust solid particle number and lung deposited surface area of compressed natural gas and diesel buses.

Because of their direct contact with society, urban buses are prioritized targets for air quality improvement. In this study, a sample group of in-use urban old buses powered by compressed natural gas (CNG) and diesel engines was chosen for particle emission analysis. The CNG buses do not have any type of after-treatment, while diesel ones are equipped with a diesel particulate filter (DPF). To measure the lung deposited surface area (LDSA), a possible physical metric of exhaust particles' toxicity, a diffusion charger-based analyzer was utilized. The measurements were done at different engine speeds in stationary conditions. The results revealed that although the particle mass emission of CNG buses remains at a low level, the number of emitted particles for 75% of the CNG buses (depending on their maintenance conditions) is 10 to 100 times more than the retrofitted diesel ones, with the range of 106 to 107 p/cm3. The rest 25% of the CNG buses were performing the same as the retrofitted diesel ones in terms of exhaust particle number in the range of 105 p/cm3. In addition, the lowest LDSA parameter at low idle engine speed was measured to be 97.8 and 229.4 µm2/cm3 for a CNG and a DPF retrofitted diesel bus, respectively. This result indicates the same and even lower LDSA and surface area and thus the lower possible toxic potentiality of exhaust particles of CNG buses compared to diesel vehicles at DPF downstream. Investigation on the different behavior of the CNG buses in the emission of particles showed the correlation of some aging parameters such as lubricant oil aging mileage with the released particles and the importance of periodic maintenance interval. Graphical abstract.