PM₁ composition and source apportionment at two sites in Delhi, India, across multiple seasons

Air pollution in urban environments has been shown to have a negative impact on air quality and human health, particularly in megacities. Over recent decades, Delhi, India, has suffered high atmospheric pollution, with significant particulate matter (PM) concentrations as a result of anthropogenic activities. Organic aerosols (OAs) are composed of thousands of different chemical species and are one of the main constituents of submicron particles. However, quantitative knowledge of OA composition, their sources and their processes in urban environments is still limited. This is important particularly in India, as Delhi is a massive, inhomogeneous conurbation, where we would expect the apportionment and concentrations to vary depending on where in Delhi the measurements/source apportionment is performed, indicating the need for multisite measurements. This study presents the first multisite analysis carried out in India over different seasons, with a focus on identifying OA sources. The measurements were taken during 2018 at two sites in Delhi, India. One site was located at the India Meteorological Department, New Delhi (ND). The other site was located at the Indira Gandhi Delhi Technical University for Women, Old Delhi (OD). Non-refractory submicron aerosol (NR-PM1) concentrations (ammonium, nitrate, sulfate, chloride and organic aerosols) of four aerosol mass spectrometers were analysed. Collocated measurements of volatile organic compounds, black carbon, NOx and CO were performed. Positive matrix factorisation (PMF) analysis was performed to separate the organic fraction, identifying a number of conventional factors: hydrocarbon-like OAs (HOAs) related to traffic emissions, biomass burning OAs (BBOAs), cooking OAs (COAs) and secondary OAs (SOAs). A composition-based estimate of PM1 is defined by combining black carbon (BC) and NR-PM1 (C-PM1= BC + NR-PM1). No significant difference was observed in C-PM1 concentrations between sites, OD (142 ± 117 µg m−3) compared to ND (123 ± 71 µg m3), from post-monsoon measurements. A wider variability was observed between seasons, where pre-monsoon and monsoon showed C-PM1 concentrations lower than 60 µg m−3. A seasonal variation in C-PM1 composition was observed; SO42- showed a high contribution over pre-monsoon and monsoon seasons, while NO3- and Cl− had a higher contribution in winter and post-monsoon. The main primary aerosol source was from traffic, which is consistent with the PMF analysis and Aethalometer model analysis. Thus, in order to reduce PM1 concentrations in Delhi through local emission controls, traffic emission control offers the greatest opportunity. PMF–aerosol mass spectrometer (AMS) mass spectra will help to improve future aerosol source apportionment studies. The information generated in this study increases our understanding of PM1 composition and OA sources in Delhi, India. Furthermore, the scientific findings provide significant information to strengthen legislation that aims to improve air quality in India.

Observing the timescales of aerosol–cloud interactions in snapshot satellite images

The response of cloud processes to an aerosol perturbation is one of the largest uncertainties in the anthropogenic forcing of the climate. It occurs at a variety of timescales, from the near-instantaneous Twomey effect to the longer timescales required for cloud adjustments. Understanding the temporal evolution of cloud properties following an aerosol perturbation is necessary to interpret the results of so-called “natural experiments” from a known aerosol source such as a ship or industrial site. This work uses reanalysis wind fields and ship emission information matched to observations of ship tracks to measure the timescales of cloud responses to aerosol in instantaneous (or“snapshot”) images taken by polar-orbiting satellites. As in previous studies, the local meteorological environment is shown to have a strong impact on the occurrence and properties of ship tracks, but there is a strong time dependence in their properties. The largest droplet number concentration (Nd) responses are found within 3 h of emission, while cloud adjustments continue to evolve over periods of 10 h or more. Cloud fraction is increased within the early life of ship tracks, with the formation of ship tracks in otherwise clear skies indicating that around 5 %–10 % of clear-sky cases in this region may be aerosol-limited. The liquid water path (LWP) enhancement and the Nd–LWP sensitivity are also time dependent and strong functions of the background cloud and meteorological state. The near-instant response of the LWP within ship tracks may be evidence of a bias in estimates of the LWP response to aerosol derived from natural experiments. These results highlight the importance of temporal development and the background cloud field for quantifying the aerosol impact on clouds, even in situations where the aerosol perturbation is clear.

Risk assessment for airborne disease transmission by poly-pathogen aerosols

In the case of airborne diseases, pathogen copies are transmitted by droplets of respiratory tract fluid that are exhaled by the infectious that stay suspended in the air for some time and, after partial or full drying, inhaled as aerosols by the susceptible. The risk of infection in indoor environments is typically modelled using the Wells-Riley model or a Wells-Riley-like formulation, usually assuming the pathogen dose follows a Poisson distribution (mono-pathogen assumption). Aerosols that hold more than one pathogen copy, i.e. poly-pathogen aerosols, break this assumption even if the aerosol dose itself follows a Poisson distribution. For the largest aerosols where the number of pathogen in each aerosol can sometimes be several hundred or several thousand, the effect is non-negligible, especially in diseases where the risk of infection per pathogen is high. Here we report on a generalization of the Wells-Riley model and dose-response models for poly-pathogen aerosols by separately modeling each number of pathogen copies per aerosol, while the aerosol dose itself follows a Poisson distribution. This results in a model for computational risk assessment suitable for mono-/poly-pathogen aerosols. We show that the mono-pathogen assumption significantly overestimates the risk of infection for high pathogen concentrations in the respiratory tract fluid. The model also includes the aerosol removal due to filtering by the individuals which becomes significant for poorly ventilated environments with a high density of individuals, and systematically includes the effects of facemasks in the infectious aerosol source and sink terms and dose calculations.

Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations

We document and evaluate the aerosol schemes as implemented in the physical and Earth system models, the Global Coupled 3.1 configuration of the Hadley Centre Global Environment Model version 3 (HadGEM3-GC3.1) and the United Kingdom Earth System Model (UKESM1), which are contributing to the sixth Coupled Model Intercomparison Project (CMIP6). The simulation of aerosols in the present-day period of the historical ensemble of these models is evaluated against a range of observations. Updates to the aerosol microphysics scheme are documented as well as differences in the aerosol representation between the physical and Earth system configurations. The additional Earth system interactions included in UKESM1 lead to differences in the emissions of natural aerosol sources such as dimethyl sulfide, mineral dust and organic aerosol and subsequent evolution of these species in the model. UKESM1 also includes a stratospheric–tropospheric chemistry scheme which is fully coupled to the aerosol scheme, while GC3.1 employs a simplified aerosol chemistry mechanism driven by prescribed monthly climatologies of the relevant oxidants. Overall, the simulated speciated aerosol mass concentrations compare reasonably well with observations. Both models capture the negative trend in sulfate aerosol concentrations over Europe and the eastern United States of America (US) although the models tend to underestimate sulfate concentrations in both regions. Interactive emissions of biogenic volatile organic compounds in UKESM1 lead to an improved agreement of organic aerosol over the US. Simulated dust burdens are similar in both models despite a 2-fold difference in dust emissions. Aerosol optical depth is biased low in dust source and outflow regions but performs well in other regions compared to a number of satellite and ground-based retrievals of aerosol optical depth. Simulated aerosol number concentrations are generally within a factor of 2 of the observations, with both models tending to overestimate number concentrations over remote ocean regions, apart from at high latitudes, and underestimate over Northern Hemisphere continents. Finally, a new primary marine organic aerosol source is implemented in UKESM1 for the first time. The impact of this new aerosol source is evaluated. Over the pristine Southern Ocean, it is found to improve the seasonal cycle of organic aerosol mass and cloud droplet number concentrations relative to GC3.1 although underestimations in cloud droplet number concentrations remain. This paper provides a useful characterisation of the aerosol climatology in both models and will facilitate understanding in the numerous aerosol–climate interaction studies that will be conducted as part of CMIP6 and beyond.

Risk assessment for airborne disease transmission by poly-pathogen aerosols

In the case of airborne diseases, pathogen copies are transmitted by droplets of respiratory tract fluid that are exhaled by the infectious and, after partial or full drying, inhaled as aerosols by the susceptible. The risk of infection in indoor environments is typically modelled using the Wells-Riley model or a Wells-Riley-like formulation, usually assuming the pathogen dose follows a Poisson distribution (mono-pathogen assumption). Aerosols that hold more than one pathogen copy, i.e. poly-pathogen aerosols, break this assumption even if the aerosol dose itself follows a Poisson distribution. For the largest aerosols where the number of pathogen in each aerosol can sometimes be several hundred or several thousand, the effect is non-negligible, especially in diseases where the risk of infection per pathogen is high. Here we report on a generalization of the Wells-Riley model and dose-response models for poly-pathogen aerosols by separately modeling each number of pathogen copies per aerosol, while the aerosol dose itself follows a Poisson distribution. This results in a model for computational risk assessment suitable for mono-/poly-pathogen aerosols. We show that the mono-pathogen assumption significantly overestimates the risk of infection for high pathogen concentrations in the respiratory tract fluid. The model also includes the aerosol removal due to filtering by the individuals which becomes significant for poorly ventilated environments with a high density of individuals, and systematically includes the effects of facemasks in the infectious aerosol source and sink terms and dose calculations.

Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations

We introduce an automated aerosol type classification method, called Source Classification ANalysis (SCAN). SCAN is based on predefined and characterized aerosol source regions, the time that the air parcel spends above each geographical region and a number of additional criteria. The output of SCAN is compared with two independent aerosol classification methods, which use the intensive optical parameters from lidar data: (1) Mahalanobis distance automatic aerosol type classification (MD) and (2) Neural Network Aerosol Typing Algorithm (NATALI). In this paper, data from the European Aerosol Research Lidar Network (EARLINET) have been used. A total of 97 free tropospheric (FT) aerosol layers from 4 typical EARLINET stations (i.e., Bucharest, Kuopio, Leipzig and Potenza) in the period 2014–2018 were classified based on a 3β+2α+1δ lidar configuration. We found that SCAN, being an optical property independent method, is not affected by the overlapping optical values of different aerosol types. Furthermore, SCAN has no limitations concerning its ability to classify different aerosol mixtures. Additionally, it is a valuable tool to classify aerosol layers, based on even to single (elastic) lidar signals, in case of lidar stations which cannot provide a full data set (3β+2α+1δ) of aerosol optical properties, therefore it can work independently of the capabilities of a lidar system. Finally, our results show that NATALI has the lower percentage of unclassified layers (4 %), while MD has the percentage of unclassified layers (50 %) and the lower percentage of cases classified as aerosol mixtures (5 %).

Observing the timescales of aerosol-cloud interactions in snapshot satellite images

The response of cloud processes to an aerosol perturbation is one of the largest uncertainties in the anthropogenic forcing of the climate. It occurs at a variety of timescales, from the near-instantaneous Twomey effect, to the longer timescales required for cloud adjustments. Understanding the temporal evolution of cloud properties following an aerosol perturbation is necessary to interpret the results of so-called "natural experiments" from a known aerosol source, such as a ship or industrial site. This work uses reanalysis windfields and ship emission information matched to observations of shiptracks to measure the timescales of cloud responses to aerosol in instantaneous (or "snapshot") images taken by polar-orbiting satellites. As found in previous studies, the local meteorological environment is shown to have a strong impact on the occurrence and properties of shiptracks, but there is a strong time dependence in their properties. The largest droplet number concentration (Nd) responses are found within three hours of emission, while cloud adjustments continue to evolve over periods of ten hours or more. Cloud fraction is increased within the early life of shiptracks, with the formation of shiptracks in otherwise clear skies indicating that around 5–10 % of clear-sky cases in this region may be aerosol-limited. The liquid water path (LWP) enhancement and the Nd-LWP sensitivity are also time dependent and strong functions of the background cloud and meteorological state. The near-instant response of the LWP within shiptracks may be evidence of a retrieval bias in previous estimates of the LWP response to aerosol derived from natural experiments. These results highlight the importance of temporal development and the background cloud field for quantifying the aerosol impact on clouds, even in situations where the aerosol perturbation is clear.

Conditional spatial interpolation method for detecting minimally polluted areas with selective aerosol emissions to the city atmosphere

Continuous surface interpolation is an important aspect of spatial analysis. A number of methods are used to interpolate a continuous surface, one of which is the spatial interpolation method with the Inverse Distance Weight (IDW) ratio. The purpose of this article is to develop a method of conditional spatial interpolation for finding such spatial points in the urban zone where the impact of selective accidental aerosol emissions into the city atmosphere is minimal. Conditional spatial interpolation refers to the case when the distances to the interpolated points are set by a certain condition, and it is necessary to determine the interpolated point where the above influence is minimal. In this case, spatial samples or base points used for interpolation are formed when a single powerful aerosol source is exposed to individual channels (distances). It is shown that there is an optimal relationship between the distances from the sampling point to the interpolation point and from the sampling point to the powerful aerosol source, at which the total effect of the powerful source on the interpolated point is minimal.

PM₁ composition and source apportionment at two sites in Delhi, India across multiple seasons

Air pollution in urban environments has been shown to have a negative impact on air quality and human health, particularly in megacities. Over recent decades, Delhi, India has suffered high atmospheric pollution, with significant particulate matter (PM) concentrations as result of anthropogenic activities. Organic aerosols (OA) are composed of thousands of different chemical species and are one of the main constituents of submicron particles. However, quantitative knowledge of OA composition, their sources and processes in urban environments is still limited. This is important particularly in India, as Delhi is a massive, inhomogeneous conurbation, which we would expect that the apportionment and concentrations will vary depending on where in Delhi the measurements/source apportionment is performed, indicating the need of multi-site measurements. This study presents the first multisite analysis carried out in India over different seasons, with a focus on identifying OA sources. The measurements were taken during 2018 at two sites in Delhi, India. One site was located at the India Meteorological Department, New Delhi (ND). The other site was located at the Indira Gandhi Delhi Technical University for Women, Old Delhi (OD). Non-refractory submicron aerosol (NR-PM1) concentrations (ammonium, nitrate, sulphate, chloride and organic aerosols) of four aerosol mass spectrometers were analysed. Collocated measurements of VOC, black carbon, NOx and CO were performed. Positive matrix factorization (PMF) analysis was performed to separate the organic fraction, identifying a number of conventional factors: hydrocarbon-like OA (HOA) related to traffic emissions, biomass burning OA (BBOA), cooking OA (COA) and secondary OA (SOA). A composition-based estimate of PM1 is defined by combining BC and NR-PM1 (C-PM1 = BC + NR-PM1). No significant difference was observed on C-PM1 concentrations between sites; OD (142 ± 117 µg m−3) compared to ND (123 ± 71 µg m−3), from post-monsoon measurements. A wider variability was observed between seasons, where pre-monsoon and monsoon showed C-PM1 concentrations lower than 60 µg m−3. A seasonal variation in C-PM1 composition was observed; SO42− showed a high contribution over pre-monsoon and monsoon seasons while NO3− and Cl− had a higher contribution in winter and post-monsoon. The main primary aerosol source was from traffic, which is consistent with the PMF analysis and aethalometer model analysis. Thus, in order to reduce PM1 concentrations in Delhi through local emission controls traffic emissions control offers the greatest opportunity. PMF-AMS mass spectra will help to improve future aerosol source apportionment studies. The information generated in this study increases our understanding of PM1 composition and OA sources in Delhi, India. Furthermore, the scientific findings provide significant information to strengthen legislation that aims to improve air quality in India.