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Air quality is the leading environmental factor for public health globally. Source apportionment of air pollution is a key aspect of understand and ameliorating air quality problems. However, its use has been rather limited primarily due to the high cost that comes with instrument deployment. The emergence of low-cost sensors provides a new tool for assessing air quality and assigning sources within outdoor and indoor environments.We have developed low-cost source apportionment techniques that can be used with single and multiple point measurements in outdoor environments. We have demonstrated its applicability for understanding sources at urban background sites, as well as assessing the pollution footprint of several industrial activities. The application of our methodology greatly improved our understanding of major pollution sources, by pinpointing and quantifying their effect in the surrounding area, at only a fraction of the cost of regulatory approaches. The information of the effect of any polluting activity, as well as the conditions that enhance or reduce it, is crucial information for the remediation of air quality problems.Continuing our successful work on apportioning pollution in outdoor environments, we now focus on the indoor environment. Indoor environments can be particularly important because of the duration of time spent within them, and hence the potential for high exposure. These environments can be highly heterogenous, with different sources and concentrations varying from room to room. In our latest study, four low-cost sensors were deployed both inside and outside a typical family house close to Birmingham, UK. While the average PM concentrations in all rooms were within the latest World Health Organisation (WHO) guidelines, great variation was found on the PM concentrations among the rooms. Using the source apportionment methods the effect of the indoor and outdoor sources of particles was quantified. Up to 95% of the PM1 was found to be from outdoor sources in all the rooms. This effect was reduced as particle size increased, though the outdoor sources were still contributing more than 65% of the PM2.5 and up to 50% of the PM10, depending on the room studied. These measurements allowed for the estimation of the average exposure of a work-at-home day. The implications on increased working from home will be discussed with respect to total exposure. This study reveals new insights into the how different indoor and outdoor sources combine within households to contribute to total air pollution exposures. It highlights that total exposure is a function of the geographic situation of the household, the physical infrastructure of the household including filtration and appliances. This methodology will also be tested and used within the RI-URBANS pilot project to assess the air quality of the area surrounding the University of Birmingham. The presentation will finish with a roadmap on how low cost source apportionment can help to improve indoor and outdoor air quality.

Traditional fixed air quality monitoring networks fulfill requirements as set in the European Air Quality Directive (2008/50/EC) and provide valuable information on ambient concentrations and temporal trends of air quality at the international, national, regional and urban level. Some short-lived pollutants or constituents, like ultrafine particle (UFPs), black carbon (BC) and nitrogen oxides (NOx), exhibit a high spatial (street-level) variability, requiring a higher monitoring resolution for more accurate exposure assessments in health or epidemiological studies. Advances in sensing and Internet of Things (IoT) technologies have resulted in smaller and more affordable stationary and mobile monitoring solutions, enabling data collection at unprecedented scales. Moreover, citizens can contribute in data collection resulting in more wide-scale data collection, dissemination and resulting impact. The collected data, however, needs adequate processing and validation in order to obtain representative exposure maps (i.e., long-term averaged concentration maps) for epidemiological studies and policy assessment.RI-URBANS aims to develop and test innovative and complementary air quality monitoring approaches in different European pilot cities. This methodological work focusses on the potential of mobile and stationary sensor applications as complementary tools for traditional (low-density) monitoring networks (Figure 1). Complementary measurements can contribute to understand spatial variability of short-lived constituents of air pollution from a diversity of pollution sources.Figure 1: Mobile and fixed sensor applications, resulting data resolution and associated requirements in terms of device (devices) and monitoring strategy (setup).We identify different data users and use cases for mobile, stationary (or combined) sensor applications and their resulting implications regarding device specifications, monitoring strategy and data processing needs. By reflecting on past studies and projects, we summarize common methodological approaches and best practices to increase the spatial resolution of air quality data. Moreover, the role of citizen engagement is evaluated, both in generating more data and air quality impact (awareness raising).This work serves as methodological input for the RI-URBANS service tools that will be tested in the pilot cities and is openly available at https://riurbans.eu/wp-content/uploads/2022/10/RI-URBANS_D13_D2.5.pdf

While the measurements of atmospheric pollutants are useful in understanding the level of the air quality at a given area, receptor models are equally important in assessing the sources of these pollutants and the extent of their effect, helping in policy making to deal with air pollution problems. Such analyses were limited and were attempted until recently only with the use of expensive regulatory-grade instruments. In the present study we applied a two-step Positive Matrix Factorisation (PMF) receptor analysis at a background site using data acquired by low-cost sensors (LCS). Using PMF, the identification of the sources that affect the air quality at the background site in Birmingham provided results that were consistent with a previous study at the site, even though in different measuring periods, but also clearly separated the anticipated sources of particulate matter (PM) and pollution. Additionally, the method supplied a metric for the contribution of different sources to the overall air quality at the site, thus providing pollution source apportionment. The use of data from regulatory-grade (RG) instruments further confirmed the reliability of the results, as well as further clarifying the particulate matter composition and origin. Comparing the results from a previous analysis, in which a k-means clustering algorithm was used, a good consistency between the results was found, and the potential and limitations of each method when used with low-cost sensor data are highlighted. The analysis presented in this study paves the way for more extensive use of LCS for atmospheric applications and receptor modelling. Here, we present the infrastructure for understanding the factors that affect the air quality at a significantly lower cost that previously possible, thus opening up multiple new opportunities for regulatory and indicative monitoring for both scientific and industrial applications.

Over the last 10 years, there has been a revolution in the use of low cost sensors to measure air pollution concentrations. These sensors are not without problems, but it is now possible to get high quality measurements of air pollutants. In particular, the use of low cost optical particle counters (OPCs) for the measurement of particulate matter (PM) in regulatory size ranges has been successfully achieved in many urban areas worldwide, with an associated cost that is far less than regulatory instruments.Successful air quality management and control not only requires measurement of air pollution levels, but it also requires information on the sources and their relative importance. Without this critical, targeted information on pollution sources, it is difficult to plan and enact control measures with which to reduce air pollution.This talk will present new work on using low cost PM sensors to achieve low cost source identification and apportionment. The measured PM size distribution obtained from OPCs is used to identify sources. Typically, source apportionment that uses particle size distributions as input relies on particle size information in the sub 300 nm size range to obtain information about sources. However, OPCs typically only provide size information above approximately 300 nm. Initial work used k-means clustering to obtain source information (Bousiotis et al. 2021). Subsequent work has used other algorithms including positive matrix factorization (PMF). Comparison between source information obtained between low cost and regulatory grade instruments is remarkably promising.This presentation will highlight the methodological approaches undertaken. It will then use a series of urban case studies showing how low cost source apportionment can be used to identify different sources of PM pollution in urban areas, both in the ambient air and indoor air. Finally, the presentation will discuss the prospects for low cost urban source apportionment and hyperlocal air pollution management.Bousiotis, D., Singh, A., Haugen, M., Beddows, D., Diez, S., Murphy, K.L., Edwards, P.M., Boies, A., Harrison, R.M. and Pope, F.D., 2021. Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors–a comparative study. Atmospheric Measurement Techniques, 14(6), pp.4139-4155. https://doi.org/10.5194/amt-14-4139-2021

Delhi is one of the world’s most polluted cities, with very high concentrations of airborne particulate matter. However, little is known on the factors controlling the characteristics of particle number size distributions. Here, new measurements are reported from three field campaigns conducted in winter, pre-monsoon and post-monsoon seasons on the Indian Insitute of Technology campus in the south of the city. Particle number size distributions were measured simultaneously using a Scanning Mobility Particle Sizer and a Grimm optical particle monitor, covering 15 nm to > 10 µm diameter. The merged, wide-range size distributions were categorised into five size ranges: nucleation (15–20 nm), Aitken (20–100 nm), accumulation (100 nm–1 µm), large fine (1–2.5 µm) and coarse (2.5–10 µm) particles. The ultrafine fraction (15–100 nm) accounts for about 52 % of all particles by number (PN10), but just 1 % by PM10 volume (PV10). The measured size distributions are markedly coarser than most from other parts of the world, but are consistent with earlier cascade impactor data from Delhi. Our results suggest substantial aerosol processing by coagulation, condensation and water uptake in the heavily polluted atmosphere, which takes place mostly at nighttime and in the morning hours. Total number concentrations are highest in winter, but the mode of the distribution is largest in the post-monsoon (autumn) season. The accumulation mode particles dominate the particle volume in autumn and winter, while the coarse mode dominates in summer. Polar plots show a huge variation between both size fractions in the same season and between seasons for the same size fraction. The diurnal pattern of particle numbers is strongly reflective of a road traffic influence upon concentrations, especially in autumn and winter. There is a clear influence of diesel traffic at nighttime when it is permitted to enter the city, and also indications in the size distribution data of a mode < 15 nm, probably attributable to CNG/LPG vehicles. New particle formation appears to be infrequent, and in this dataset is limited to one day in the summer campaign. Our results reveal that the very high emissions of airborne particles in Delhi, particularly from traffic, determine the variation of particle number size distributions.

Measurement and source apportionment of atmospheric pollutants is crucial for the assessment of air quality and the implementation of policies for its improvement. In most cases, such measurements use expensive regulatory grade instruments, which makes it difficult to achieve wide spatial coverage. Low-cost sensors may provide a more affordable alternative, but their capability and reliability in separating distinct sources of particles have not been tested extensively yet. The present study examines the ability of a low-cost Optical Particle Counter (OPC) to identify the sources of particles and conditions that affect particle concentrations at an urban background site in Birmingham, UK. To help evaluate the results, the same analysis is performed on data from a regulatory-grade instrument (SMPS) and compared to the outcomes from the OPC analysis. The low-cost sensor analysis manages to separate periods and atmospheric conditions according to the level of pollution at the site. It also successfully identifies a number of sources for the observed particles, which were also identified using the regulatory-grade instruments. The low-cost sensor, due to the particle size range measured (0.35 to 40 μm), performed rather well in differentiating sources of particles with sizes greater than 1 μm, though its ability to distinguish their diurnal variation, as well as to separate sources of smaller particles, at the site was limited. The current level of source identification demonstrated makes the technique useful for background site studies, where larger particles with smaller temporal variations are of significant importance. This study highlights the current capability of low-cost sensors in source identification and differentiation using clustering approaches. Future directions towards particulate matter source apportionment using low cost OPCs are highlighted.

New particle formation (NPF) events occur almost everywhere in the world and can play an important role as a particle source. The frequency and characteristics of NPF events vary spatially and this variability is yet to be fully understood. In the present study, long term particle size distribution datasets (minimum of three years) from thirteen sites of various land uses and climates from across Europe were studied and NPF events, deriving from secondary formation and not traffic related nucleation, were extracted and analysed. The frequency of NPF events was consistently found to be higher at rural background sites, while the growth and formation rates of newly formed particles were higher at roadsides, underlining the importance of the abundance of condensable compounds of anthropogenic origin found there. The growth rate was higher in summer at all rural background sites studied. The urban background sites presented the highest uncertainty due to greater variability compared to the other two types of site. The origin of incoming air masses and the specific conditions associated with them greatly affect the characteristics of NPF events. In general, cleaner air masses present higher probability for NPF events, while the more polluted ones show higher growth rates. However, different patterns of NPF events were found even at sites in close proximity (

Although new particle formation (NPF) events have been studied extensively for some decades, the mechanisms that drive their occurrence and development are yet to be fully elucidated. Laboratory studies have done much to elucidate the molecular processes involved in nucleation, but this knowledge has yet to be linked to NPF events in the atmosphere, except at very clean air sites. There is great difficulty in successful application of the results from laboratory studies to real atmospheric conditions, due to the diversity of atmospheric conditions and observations found, as NPF events occur almost everywhere in the world without following a clearly defined trend of frequency, seasonality, atmospheric conditions or event development. The present study seeks common features in nucleation events by applying a binned linear regression over an extensive dataset from 16 sites of various types (rural and urban backgrounds as well as roadsides) in Europe. A clear positive relation is found between the solar radiation intensity, temperature and atmospheric pressure with the frequency of NPF events, while relative humidity presents a negative relation with NPF event frequency. Wind speed presents a less consistent relationship which appears to be heavily affected by local conditions. While some meteorological variables appear to have a crucial effect on the occurrence and characteristics of NPF events, especially at rural sites, it appears that their role becomes less marked when at higher values. The analysis of chemical composition data presents interesting results. Concentrations of almost all chemical compounds studied (apart from O3) and the Condensation Sink (CS) have a negative relation with NPF event probability, though areas with higher average concentrations of SO2 had higher NPF event probability. Particulate Organic Carbon (OC), Volatile Organic Compounds (VOCs) and particulate phase sulphate consistently had a positive relation with the growth rate of the newly formed particles. As with some meteorological variables, it appears that at increased concentrations of pollutants or the CS, their influence upon NPF probability is reduced.

16 pages, 7 figures, 2 tables, supplement https://dx.doi.org/10.5194/acp-19-5679-2019, New particle formation (NPF) events have different patterns of development depending on the conditions of the area in which they occur. In this study, particle size distributions in the range of 16.6–604 nm (7 years of data) were analysed and NPF events occurring at three sites of differing characteristics – rural Harwell (HAR), urban background North Kensington (NK), urban roadside Marylebone Road (MR), London, UK – were extracted and studied. The different atmospheric conditions in each study area not only have an effect on the frequency of the events, but also affect their development. The frequency of NPF events is similar at the rural and urban background locations (about 7 % of days), with a high proportion of events occurring at both sites on the same day (45 %). The frequency of NPF events at the urban roadside site is slightly less (6 % of days), and higher particle growth rates (average 5.5 nm h−1 at MR compared to 3.4 and 4.2 nm h−1 at HAR and NK respectively) must result from rapid gas-to-particle conversion of traffic-generated pollutants. A general pattern is found in which the condensation sink increases with the degree of pollution of the site, but this is counteracted by increased particle growth rates at the more polluted location. A key finding of this study is that the role of the urban environment leads to an increment of 20 % in N16–20 nm in the urban background compared to that of the rural area in NPF events occurring at both sites. The relationship of the origin of incoming air masses is also considered and an association of regional events with cleaner air masses is found. Due to lower availability of condensable species, NPF events that are associated with cleaner atmospheric conditions have lower growth rates of the newly formed particles. The decisive effect of the condensation sink in the development of NPF events and the survivability of the newly formed particles is underlined, and influences the overall contribution of NPF events to the number of ultrafine particles in an area. The other key factor identified by this study is the important role that pollution, both from traffic and other sources in the urban environment (such as heating or cooking), plays in new particle formation events, The authors acknowledge financial support (to DCSB) from the Natural Environment Research Council's funding of the National Centre for Atmospheric Science (NCAS) (grant number R8/H12/83/011)