Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area

Abstract

We performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20–400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO2. Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 μm2 cm–3) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 μm2 cm–3) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO2) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM2.5.

Machine Learning Models for Ultrafine Particles in Copenhagen, Denmark

“…The LDSA in such areas were as high as 25 µm 2 /cm 3 in London. 40 In our modeling practice in Copenhagen, the tra c related variables were the most in uential predictors of LDSA, but distance to restaurants, café, or pubs was also an important predictor, supporting the role of such sources in contribution to LDSA (Table S6).…”

“…69 In London, UK, the average LDSA is reported to be near 18 µm 2 /cm 3 . 40 In Switzerland, across four study areas (Basel, Geneva, Lugano, Wald), the mean LDSA was 32.1 µm 2 /cm 3 . 25 The average PS of UFP strongly relates to its source and various conditions in the production process.…”

“…42 The satellite observation class included 6 predictors for normalized difference vegetation index (NDVI) from Landsat satellite for the year 2019 at ~ 30 m spatial resolution, also at different buffers (30,100,200,300,400, and 500 m radii). The land use class comprised of 10 predictors for the area of parks within 100, 200, 300, 400, and 500 m buffers, and additionally area of residential land use again within 100, 200, 300, 400, and 500 m buffer radii at a spatial resolution of 1 m. The tra c class included 31 predictors at a 1 m spatial resolution, such as 2017 annual average daily tra c, and length of various major roads in different categories and speed limits within buffers 100, 125, 150, 200, 250, 300, 350, 400, 450, and 500 m. Finally, the distance class included 42 predictors, also at a 1 m spatial resolution, such as distance to Open Street Map (OSM) major roads, major roads with different speed limits (40,50,60,70, and 100 km/h), one way roads, two-way roads, primary, secondary, and tertiary roads, tunnels, bridges, airport, gas stations, parking lots, tra c signals, parking areas, schools, kindergartens, railways, taxi stations, ferry terminals, bus stops, bus stations, restaurants, café, or pubs, supermarkets, malls, hotels, parks, industries, inland water bodies, amongst others. Overall, the spatial resolution of 84 of 94 predictors (~ 90%) was 1 m.…”

“…More recently, mobile monitoring using Google Street View cars with intensive repeated day-time on-road measurements during weekdays has been used to develop novel models for UFP. [38][39][40] In this study, we aimed to develop very ne spatial resolution machine learning (ML) models to estimate long-term mean UFP metrics, namely PNC, LDSA, and particle size (PS) in the capital city of Copenhagen, Denmark, using standard designed campaigns and technologies for monitoring and state-of-the-art techniques for modeling.…”

Integrated Assessment Modelling of Future Air Quality in the UK to 2050, and Synergies with Net Zero Strategies

“…These include non-exhaust emissions from road transport and domestic combustion of wood, which are examined further in Section 4, below. Additional uncertainties have been reported elsewhere [3,12], notably emissions from cooking which are not included in the NAEI but have been found to have a significant impact on exposure to PM2.5 [13], and the non-linear response of SIA to changes in pre-cursor emissions [14].…”