Sources of particle number concentration and noise near London Gatwick Airport

Particle number size distributions and concentrations in transportation environments: a review

Ambient air ultrafine particles (UFP, particles with a diameter <100 nm) have gained significant attention in World Health Organization (WHO) air quality guidelines and European legislation. This review explores UFP concentrations and particle number size distributions (PNC-PNSD) in various transportation hotspots, including road traffic, airports, harbors, trains, and urban commuting modes (walking, cycling, bus, tram, and subway). The results highlight the lack of information on personal exposure at harbors and railway stations, inside airplanes and trains, and during various other commuting modes. The different lower particle size limits of the reviewed measurements complicate direct comparisons between them. Emphasizing the use of instruments with detection limits ≤10 nm, this review underscores the necessity of following standardized UFP measurement protocols. Road traffic sites are shown to exhibit the highest PNC within cities, with PNC and PNSD in commuting modes driven by the proximity to road traffic and weather conditions. In closed environments, such as cars, buses, and trams, increased external air infiltration for ventilation correlates with elevated PNC and a shift in PNSD toward smaller diameters. Airports exhibit particularly elevated PNCs near runways, raising potential concerns about occupational exposure. Recommendations from this study include maintaining a substantial distance between road traffic and other commuting modes, integrating air filtration into ventilation systems, implementing low-emission zones, and advocating for a general reduction in road traffic to minimize daily UFP exposure. Our findings provide important insights for policy assessments and underscore the need for additional research to address current knowledge gaps.

Quantifying the contributions of road and air traffic to ambient ultrafine particles in two urban communities

Traffic-related activities are widely acknowledged as a primary source of urban ambient ultrafine particles (UFPs). However, a notable gap exists in quantifying the contributions of road and air traffic to size-resolved and total UFPs in urban areas. This study aims to delineate and quantify the traffic's contributions to size-resolved and total UFPs in two urban communities. To achieve this, stationary sampling was conducted at near-road and near-airport communities in Seattle, Washington State, to monitor UFP number concentrations during 2018-2020. Comprehensive correlation analyses among all variables were performed. Furthermore, a fully adjusted generalized additive model, incorporating meteorological factors, was developed to quantify the contributions of road and air traffic to size-resolved and total UFPs. The study found that vehicle emissions accounted for 29% of total UFPs at the near-road site and 13% at the near-airport site. Aircraft emissions contributed 14% of total UFPs at the near-airport site. Notably, aircraft predominantly emitted UFP sizes below 20 nm, while vehicles mainly emitted UFP sizes below 50 nm. These findings reveal the variability in road and air traffic contributions to UFPs in distinct areas. Our study emphasizes the pivotal role of traffic layout in shaping urban UFP exposure.

Source apportionment of fine and ultrafine particle number concentrations in a major city of the Eastern Mediterranean

Ultrafine particles (UFP) are recognized as an emerging pollutant able to induce serious health effects. However, quantitative information regarding the contributions of UFP sources is generally limited. This study evaluates statistical (k-means clustering) and receptor models (Positive Matrix Factorization - PMF) using particle number size distributions (PNSD), along with chemical speciation data, measured at an urban background supersite in Athens, Greece, aiming to characterize their sources. PNSD measurements (10-487 nm) were performed during three distinct periods (warm, cold, and lockdown cold). Traffic and residential biomass burning (BB) produced high UFP number concentrations (NUFP) in the cold period (+107 % compared to summer), while the lockdown restrictions reduced NUFP (-42 %). The five groups produced by cluster analysis that were common among periods were linked to high- and low-traffic, new particle formation (NPF), urban background and regional aerosols. PMF source apportionment identified 5 and 6 factors during warm and cold periods, respectively, indicating that traffic particles dominated NUFP (64-78 % in all periods), while accumulation-mode particles and volume concentrations were controlled by processed aerosol, and especially in the cold periods by BB emissions. A nucleation factor linked to NPF contributed 7-11 % to NUFP. Comparing the two cold periods (business-as-usual, lockdown), important lockdown reductions (-46 %) were seen for fresh traffic contributions to total number concentration (Ntotal). The impact of the source attributed to NPF also eroded (-41 % for Ntotal). Due to the large reduction (-47 % for Ntotal) observed also for the BB source during the lockdown (reduced wood usage due to a milder winter), the relative contributions of all sources did not change considerably (fractional reductions <7 % for Ntotal). The quantitative results, bolstered by source apportionment combining PNSD and online chemical composition measurements, indicate the potential to constrain UFP levels by regulating traffic and residential emissions, with a large upside for population exposure control.

The Berlin-Brandenburg Air Study-A Methodological Study Paper of a Natural Experiment Investigating Health Effects Related to Changes in Airport-Related Exposures

Objectives: This paper presents the study design of the Berlin-Brandenburg Air study (BEAR-study). We measure air quality in Berlin and Brandenburg before and after the relocation of aircraft (AC) traffic from Tegel (TXL) airport to the new Berlin-Brandenburg airport (BER) and investigate the association of AC-related ultrafine particles (UFP) with health outcomes in schoolchildren. Methods: The BEAR-study is a natural experiment examining schoolchildren attending schools near TXL and BER airports, and in control areas (CA) away from both airports and associated air corridors. Each child undergoes repeated school-based health-examinations. Total particle number concentration (PNC) and meteorological parameters are continuously monitored. Submicrometer particle number size distribution, equivalent black carbon, and gas-phase pollutants are collected from long-term air quality monitoring stations. Daily source-specific UFP concentrations are modeled. We will analyze short-term effects of UFP on respiratory, cardiovascular, and neurocognitive outcomes, as well as medium and long-term effects on lung growth and cognitive development. Results: We examined 1,070 children (as of 30 November 2022) from 16 schools in Berlin and Brandenburg. Conclusion: The BEAR study increases the understanding of how AC-related UFP affect children's health.

Measurement of road traffic brake and tyre dust emissions using both particle composition and size distribution data

Estimates of tyre and brake wear emission factors are presented, derived from data collected from roadside and urban background sites on the premises of the University of Birmingham, located in the UK's second largest city. Size-fractionated particulate matter samples were collected at both sites concurrently in the spring/summer of 2019 and analysed for elemental concentrations and magnetic properties. Using Positive Matrix Factorisation (PMF), three sources were identified in the roadside mass increment of the 1.0-9.9 μm stages of MOUDI impactors located at both sites, namely: brake dust (7.1%); tyre dust (9.6%); and crustal (83%). The large fraction of the mass apportioned to crustal material was suspected to be mainly from a nearby construction site rather than resuspension of road dust. By using Ba and Zn as elemental tracers, brake and tyre wear emission factors were estimated as 7.4 mg/veh.km and 9.9 mg/veh.km, respectively, compared with the PMF-derived equivalent values of 4.4 mg/veh.km and 11 mg/veh.km. Based on the magnetic measurements, an emission factor can be estimated independently for brake dust of 4.7 mg/veh.km. A further analysis was carried out on the concurrently measured roadside increment in the particle number size distribution (10 nm-10 μm). Four factors were identified in the hourly measurements: traffic exhaust nucleation; traffic exhaust solid particles; windblown dust; and an unknown source. The high increment of the windblown dust factor, 3.2 μg/m³, was comparable in magnitude to the crustal factor measured using the MOUDI samples (3.5 μg/m³). The latter's polar plot indicated that this factor was dominated by a large neighbouring construction site. The number emission factors of the exhaust solid particle and exhaust nucleation factors were estimated as 2.8 and 1.9 x 10¹²/veh.km, respectively.

A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution

The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.