Sources of particle number concentration and noise near London Gatwick Airport.

There is increasing evidence of potential health impacts from both aircraft noise and aircraft-associated ultrafine particles (UFP). Measurements of noise and UFP are however scarce near airports and so their variability and relationship are not well understood. Particle number size distributions and noise levels were measured at two locations near Gatwick airport (UK) in 2018-19 with the aim to characterize particle number concentrations (PNC) and link PNC sources, especially UFP, with noise. Positive Matrix Factorization was used on particle number size distribution to identify these sources. Mean PNC (7500-12,000 p cm-3) were similar to those measured close to a highly trafficked road in central London. Peak PNC (94,000 p cm-3) were highest at the site closer to the runway. The airport source factor contributed 17% to the PNC at both sites and the concentrations were greatest when the respective sites were downwind of the runway. However, the main source of PNC was associated with traffic emissions. At both sites noise levels were above the recommendations by the WHO (World Health Organisation). Regression models of identified UFP sources and noise suggested that the largest source of noise (LAeq-1hr) above background was associated with sources of fresh traffic and urban UFP depending on the site. Noise and UFP correlations were moderate to low suggesting that UFP are unlikely to be an important confounder in epidemiological studies of aircraft noise and health. Correlations between UFP and noise were affected by meteorological factors, which need to be considered in studies of short-term associations between aircraft noise and health.

Source attribution and quantification of atmospheric nickel concentrations in an industrial area in the United Kingdom (UK).

Pontardawe in South Wales, United Kingdom (UK), consistently has the highest concentrations of nickel (Ni) in PM10 in the UK and repeatedly breaches the 20 ng m-3 annual mean EU target value. Several local industries use Ni in their processes. To assist policy makers and regulators in quantifying the relative Ni contributions of these industries and developing appropriate emission reduction approaches, the hourly concentrations of 23 elements were measured using X-ray fluorescence alongside meteorological variables and black carbon during a four-week campaign in November-December 2015. Concentrations of Ni ranged between 0 and 2480 ng m-3 as hourly means. Positive Matrix Factorization (PMF) was used to identify sources contributing to measured elements. Cluster analysis of bivariate polar plots of those factors containing Ni in their profile was further used to quantify the industrial processes contributing to ambient PM10 concentrations. Two sources were identified to contribute to Ni concentrations, stainless-steel (which contributed to 10% of the Ni burden) and the Ni refinery (contributing 90%). From the stainless-steel process, melting activities were responsible for 66% of the stainless-steel factor contribution.

European aerosol phenomenology - 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets.

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30-240 min) and long-term data coverage (9-36 months), providing essential information to improve/validate air quality, health impact, and climate models.

Air quality in enclosed railway stations: Quantifying the impact of diesel trains through deployment of multi-site measurement and random forest modelling.

Concentrations of the air pollutants (NO2 and particulate matter) were measured for several months and at multiple locations inside and outside two enclosed railway stations in the United Kingdom - Edinburgh Waverly (EDB) and London King's Cross (KGX) - which, respectively, had at the time 59% and 18% of their train services powered by diesel engines. Average concentrations of NO2 were above the 40 µg m-3 annual limit value outside the stations and were further elevated inside, especially at EDB. Concentrations of PM2.5 inside the stations were 30-40% higher at EDB than outside and up to 20% higher at KGX. Concentrations of both NO2 and PM2.5 were highest closer to the platforms, especially those with a higher frequency of diesel services. A random-forest regression model was used to quantify the impact of numbers of different types of diesel trains on measured concentrations allowing prediction of the impact of individual diesel-powered rolling stock.