ABSTRACT
The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.
ABSTRACT
The aim of this study is to quantify the BIAS in air pollution (PM2.5, NO2) exposure estimates that arise from neglecting population activity under COVID-19 lockdown conditions. We applied mobility data as derived from different sources (Google, Eurostat, Automatic Identification System, etc.) to model the impact of (1) changing emissions and (2) the change in population activity patterns in a European multi-city (Hamburg, Liège, Marseille) exposure study. Our results show significant underestimations of exposure estimates when activity profiles are either neglected or not adjusted for lockdown conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic altered the human mobility and economic activities immensely, as authorities enforced unprecedented lock down regulations. In order to reduce the spread of COVID-19, a complete lockdown was observed between 24 March - 31 May 2020 in Pakistan. This paper aims at investigating the PM2.5, AOD and column amounts of six trace gases (NO2, SO2, CH4, HCHO, C2H2O2, and O3) by comparing periods of reduced emissions during lockdown periods with reference periods without emission reductions over Lahore, Pakistan. HYSPLIT cluster trajectory analyses were performed, which confirmed similar meteorological flow conditions during lockdown and reference periods. This provides confidence that any change in air quality conditions would be due to changes in human activities and associated emissions. The results show about 38% reduction in ambient surface PM2.5 levels during the lockdown period. This change also positively correlated with MODISDB and AERONETAOD data with a decrease of AOD by 42% and 35%, respectively. Reductions for tropospheric columns of NO2 and SO2 were about 20% and 50%, respectively during a semi lockdown period, while no reduction in the CH4, C2H2O2, HCHO and O3 levels occurred. During the lockdown period NO2, O3 and CH4 were about 50%, 45% and 25% lower, respectively, but no reduction in SO2, C2H2O2 and HCHO levels were noticed compared to the reference lockdown period for Lahore. HYSPLIT cluster trajectory analysis revealed the greatest impact on Lahore air quality through local emissions and regional transport from the east (agricultural burning and industry).
ABSTRACT
Activity restrictions implemented to control the spread of the COVID-19 virus imposed a significant effect on the air quality of cities across the world. The initiative of the World Meteorological Organisation/Global Atmospheric Watch for studying effects of the 2020 COVID-19 lockdowns on air quality has produced two sets of analysis results for cities across the world, based on observational data and modelling, respectively. The modelling study aims to evaluate the modelling tools in a regime involving significant changes of activity, and at the same provide insights on the effect of selectively reduced emissions on the chemistry and composition of urban pollutants. For most of the cities, a reduction on NOx average concentrations between 11% and 70% was calculated for the lockdown period, while PM10 was reduced by 8% up to 35% in a good agreement with measured reductions observed during the 2020 lockdown period compared to the corresponding period of 2019. Taking advantage of an operational Air Quality Modelling System, which is in continuous application in the cities of Thessaloniki and Nicosia, the contribution of sectoral emissions and the role of meteorology over the observed concentration reductions was assessed. The study reveals that in both cities, observed reductions of urban PM2.5, PM10 and NOx concentration patterns can be mainly attributed to the corresponding emissions reductions in the transport and heating sectors, while O3 is strongly affected by titration near the city centre. At the same time, meteorological patterns appear to strongly influence and even mask these effects in terms of daily averages, while the impact of imposed large-scale boundary conditions on the modelling results can also be significant. © British Crown Copyright (2022)
ABSTRACT
Emissions of monoterpenes from volatile chemical products (VCPs) have been shown to contribute substantially to secondary organic aerosol (SOA) formation in a number of cities across the US. In the southeast US (SE US), monoterpenes are the dominant precursor to SOA production. Previous studies have assumed that monoterpenes are primarily biogenic in origin. We examine that assumption here using a total of nine months of volatile organic compound (VOC) observations spanning across 2020 and 2021 which are made via proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) in Atlanta, Georgia. The dataset is resolved via non-negative matrix factorization (NNMF) into two biogenic, four anthropogenic, and two secondary factors. NNMF results show that VCP sources contribute to 0.28 ± 0.17 ppb of monoterpenes, or about 26% of all monoterpenes in the summer and 53% in the winter. Interannual comparison suggest minimum impact of the COVID-19 pandemic on atmospheric VOC composition in Atlanta during the second half of 2020. Comparison of NNMF anthropogenic monoterpenes-to-benzene ([Manthro/B]) and anthropogenic-to-total monoterpenes ([Manthro/MT]) ratios with emission ratios in the National Emission Inventory 2016v1 modeling platform (NEI16) and Fuel-Based Inventory of Vehicle Emissions and Volatile Chemical Products (FIVE-VCP) suggests that NEI16 underestimates anthropogenic monoterpene emissions by as much as 70% in Atlanta. FIVE-VCP estimates higher anthropogenic monoterpene emissions than NEI16 and provides better agreement with observations, especially during winter months. Anthropogenic monoterpenes may impact as much as 17% of total summertime SOA formation in Atlanta and the SE US, with potential additional influence of limonene from anthropogenic monoterpene sources due to its higher reactivity and SOA yield potential.
ABSTRACT
To control the spread of COVID-19, exceptional restrictive measures were taken in March 2020 that imposed a radical change on the lifestyle of millions of citizens around the world, albeit for a short period. The national lockdown, which lasted from 10 March to 18 May 2020 in Italy, was a unique opportunity to observe the variation in air quality in urban environments under conditions comprising almost total traffic restriction and a strong reduction in work activities. In this paper, the data from 17 urban monitoring sites in Tuscany are presented, and the PM and NO2 concentrations in the 2 months before the start of the lockdown and the 2 months after lockdown are compared with the corresponding months of the previous 3 years. The results show that the total loads of PM2.5 and PM10 (particulate matter with an aerodynamic diameter smaller than 2.5 and 10 µm, respectively) decreased, but they did not exhibit significant changes compared to previous years, whereas NO2 underwent a drastic reduction. For three of these sites, the chemical composition of the collected samples was measured using thermal–optical techniques, ion chromatography, and particle-induced X-ray emission analysis, and the application of multivariate positive matrix factorization analysis also allowed for PM10 source identification and apportionment. Using these analyses, it was possible to explain the low sensitivity of PM10 to the lockdown effects as being due to different, sometimes inverse, behaviors of the different sources that contribute to PM. The results clearly indicate a decline in pollution levels related to urban traffic and an increase in the concentration of sulfate for all sites during the lockdown period.
ABSTRACT
Air pollution is considered a major public health risk globally, and the global South including sub-Saharan Africa face particular health risks, but there is limited data to quantify the level of pollution for different air quality contexts. The COVID-19 lockdown measures led to reduced human activities, and provided a unique opportunity to explore the impacts of reduced activities on urban air quality. This paper utilises calibrated data from a low-cost sensor network to explore insights from the diverse ambient air quality profile for four urban locations in Greater Kampala, Uganda before and during lockdown from March 31 to May 5 2020, highlighting the uniqueness of air pollution profiles in a sub-Saran Africa context. All locations saw year to year improvements in 24-hour mean PM2.5 between 9 and 25µg/m3 (i.e. 17-50% reduction from the previous year) and correlated well with reduction in traffic (up to approx. 80%) and commercial activities. The greatest improvement was observed in locations close to major transport routes in densely populated residential areas between 8 pm and 5 am. This suggests that the reduction in localised pollution sources such as nocturnal polluting activities including traffic and outdoor combustion including street cooking characteristic of fast-growing cities in developing countries, coupled with meteorological effects led to amplified reductions that continued well into the night, although meteorological effects are more generalised. Blanket policy initiatives targeting peak pollution hours could be adopted across all locations, while transport sector regulation could be very effective for pollution management. Likewise, because of the clustered and diffuse nature of pollution, community driven initiatives could be feasible for long-term mitigation. © 2022
ABSTRACT
The COVID-19 pandemic has drastically changed human life and the world's environment. Most of the major cities of the USA went under full or partial lockdown in the first half of 2020. However, it started gradually reopening, and in 2021, most of the public activity restrictions were lifted. Many studies reported a significant improvement in air quality during the COVID-19 pandemic in the USA, corresponding with the reduced human activity. We hypothesized that this improved air quality was followed by the decline to air quality again due to the normalization of human activity in 2021. This study is a novel approach of studying air quality using spatio-temporal analysis at the finer spatial level within a city in the USA. It assessed the change in six air quality parameters from the pre-COVID era to the post-COVID era in Chicago city in Illinois, USA. The study found that reduced human activities during COVID-19, improved air quality by reducing the concentration of some air pollutants, especially PM2.5, NO2, and CO. However, this improvement was transitory, and it reverted in the post-COVID era. Therefore, policies should be formulated and practiced to improve air quality in the long term.
ABSTRACT
Diesel-powered vehicles emit several times more nitrogen oxides than comparable gasoline-powered vehicles, leading to ambient nitrogen dioxide (NO2) pollution and adverse health impacts. The COVID-19 pandemic and ensuing changes in emissions provide a natural experiment to test whether NO2 reductions have been starker in regions of Europe with larger diesel passenger vehicle shares. Here we use a semi-empirical approach that combines in-situ NO2 observations from urban areas and an atmospheric composition model within a machine learning algorithm to estimate business-as-usual NO2 during the first wave of the COVID-19 pandemic in 2020. These estimates account for the moderating influences of meteorology, chemistry, and traffic. Comparing the observed NO2 concentrations against business-as-usual estimates indicates that diesel passenger vehicle shares played a major role in the magnitude of NO2 reductions. European cities with the five largest shares of diesel passenger vehicles experienced NO2 reductions ∼2.5 times larger than cities with the five smallest diesel shares. Extending our methods to a cohort of non-European cities reveals that NO2 reductions in these cities were generally smaller than reductions in European cities, which was expected given their small diesel shares. We identify potential factors such as the deterioration of engine controls associated with older diesel vehicles to explain spread in the relationship between cities’ shares of diesel vehicles and changes in NO2 during the pandemic. Our results provide a glimpse of potential NO2 reductions that could accompany future deliberate efforts to phase out or remove passenger vehicles from cities.
ABSTRACT
In this article, we aim to show the capabilities, benefits, as well as restrictions, of three different air quality-related information sources, namely the Sentinel-5Precursor TROPOspheric Monitoring Instrument (TROPOMI) space-born observations, the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based measurements and the LOng Term Ozone Simulation-EURopean Operational Smog (LOTOS-EUROS) chemical transport modelling system simulations. The tropospheric NO2 concentrations between 2018 and 2021 are discussed as air quality indicators for the Greek cities of Thessaloniki and Ioannina. Each dataset was analysed in an autonomous manner and, without disregarding their differences, the common air quality picture that they provide is revealed. All three systems report a clear seasonal pattern, with high NO2 levels during wintertime and lower NO2 levels during summertime, reflecting the importance of photochemistry in the abatement of this air pollutant. The spatial patterns of the NO2 load, obtained by both space-born observations and model simulations, show the undeniable variability of the NO2 load within the urban agglomerations. Furthermore, a clear diurnal variability is clearly identified by the ground-based measurements, as well as a Sunday minimum NO2 load effect, alongside the rest of the sources of air quality information. Within their individual strengths and limitations, the space-borne observations, the ground-based measurements, and the chemical transport modelling simulations demonstrate unequivocally their ability to report on the air quality situation in urban locations.
ABSTRACT
During the period of the COVID-19 pandemic, the air quality reached the best levels to be recorded in large cities in Spain. To analyze and demonstrate this improvement in air quality levels, the evolution of the average nitrogen dioxide (NO2) levels in 78 Spanish cities with more than 50,000 inhabitants during the pre-COVID-19 years (2017–2019), the period of the COVID-19 lockdown, and the post-COVID-19 year (2021) was analyzed. The results show an improvement in the air quality in most of the cities analyzed for 2020 due to the COVID-19 restrictions. In addition, in 2021, without the COVID-19 restrictions, the air quality levels of the largest cities in Spain showed important improvements in terms of NO2 concentration compared to the levels in the pre-COVID-19 years (2017–2019). Nevertheless, in 2021, only 11 cities were below the average annual limit of 10 µg/m3 NO2 established by the World Health Organization (WHO). In addition, no cities with more than 500,000 inhabitants achieved NO2 levels below the WHO limit. Finally, a detailed monthly analysis indicated that the pre-COVID-19 levels were reached again during the last months of the monitored period.
ABSTRACT
We present the effects of the confinement and physical distancing policies applied during the COVID-19 pandemic on the concentrations of PM10, PM2.5, NO, NO2 and O3 in 16 cities in central and southern Chile. The period between March and May in 2020 was compared with the corresponding months during 2017–2019, using surface data and satellite information. The relative percent changes in the concentration of atmospheric pollutants, and the meteorological variables observed between these two periods were used to quantify the effects of the lockdowns on the local air quality of the urban areas studied. The results showed statistically significant changes in 11 of the 16 cities. Significant relative changes between +14% and –33% were observed for PM10 in 9 cities;while statistically significant changes between –6% and –48% were evident for PM2.5 in 10 cities. Significant decreases between –27% and –55%, were observed in 4 cities in which NO2 data were available;while significant increases in O3, between 18% and 43%, were found in 4 of the 5 cities with available data. The local meteorological variables did not show significant changes between both periods. In all the cities studied, one of the main PM sources is wood burning for residential heating. Although the quarantine imposed during the health emergency could have induced an increase in residential emissions, these were compensated with the reductions in vehicular and/or industrial emissions. Therefore, these results should be carefully interpreted and should inspire new research considering the social, cultural, and economic factors that could alter the common emission patterns and air quality of urban centers.
ABSTRACT
The conflict between economic growth and environmental pollution has become a considerable bottleneck to future development throughout the world. The industrial structure may become the possible key factor in resolving the contradiction. Using the daily data of air quality from January to April in 2019 and 2020, we used the DID model to identify the effects of industrial structure on air quality by taking the COVID-19 pandemic as a quasi-experiment. The results show that, first, the impact of profit of the secondary industry on air quality is ten times higher than that of the tertiary industry. Therefore, the secondary industry is the main factor causing air pollution. Second, the effect of the reduction in the secondary industry on the improvement of air quality is better than that of the tertiary industry in Beijing. Therefore, the implementation of Beijing’s non-capital function relief policy is timely and reasonable, and the adjustment of the industrial structure is effective in the improvement of air quality. Third, PM2.5, NO2, and CO are affected by the secondary and tertiary industries, where PM2.5 is affected most seriously by the second industry. Therefore, the transformation from the secondary industry to the tertiary industry can not only solve the problem of unemployment but also relieve the haze. Fourth, the result of O3 is in opposition to other pollutants. The probable reason is that the decrease of PM2.5 would lead to an increase in the O3 concentration. Therefore, it is difficult to reduce O3 concentrationby production limitation and it is urgent to formulate scientific methods to deal with O3 pollution. Fifth, the air quality in the surrounding areas can also influence Beijing. As Hebei is a key area to undertake Beijing’s industry, the deterioration of its air quality would also bring pressure to Beijing’s atmospheric environment. Therefore, in the process of industrial adjustment, the selection of appropriate regions for undertaking industries is very essential, which is worth our further discussion.
ABSTRACT
Strict restrictions to halt the spread of COVID-19 provided an opportunity to quantify the contribution of different pollution agents. We analyze the concentrations of pollutants recorded in Rome during the lockdown periods for the containment of the spread of Covid 19, compared with those of other periods and years. We recorded a significant contribution attributable to heating systems powered by fuel. Thus, we propose the replacement of existing boilers for heating and drinking hot water (DHW) production systems, with air / water heat pumps, as an intervention to improve urban air quality. We analyze the replacement scenarios, within the entire residential building stock in the Municipality of Rome, in terms of emissions reduction, primary energy savings and reduced CO2 production. Results show significant reductions in concentrations. Reduction in primary energy consumption varies between 12% and 56% for various scenarios, different for outdoor temperatures and mix of electricity generation. The intervention on the urban scale can reduce air pollution on a long-term basis, implying significant reductions of polluting emissions in urban areas, and entailed reduced energy (and therefore environmental) costs, with a significant step towards sustainable cities.
ABSTRACT
In recent years, many surveillance cameras have been installed in the Greater Taipei Area, Taiwan; traffic data obtained from these surveillance cameras could be useful for the development of roadway-based emissions inventories. In this study, web-based traffic information covering the Greater Taipei Area was obtained using a vision-based traffic analysis system. Web-based traffic data were normalized and applied to the Community Multiscale Air Quality (CMAQ) model to study the impact of vehicle emissions on air quality in the Greater Taipei Area. According to an analysis of the obtained traffic data, sedans were the most common vehicles in the Greater Taipei Area, followed by motorcycles. Moderate traffic conditions with an average speed of 30-50 km/h were most prominent during weekdays, whereas traffic flow with an average speed of 50-70 km/h was most common during weekends. The proportion of traffic flows in free-flow conditions (>70 km/h) was higher on weekends than on weekdays. Two peaks of traffic flow were observed during the morning and afternoon peak hours on weekdays. On the weekends, this morning peak was not observed, and the variation in vehicle numbers was lower than on weekdays. The simulation results suggested that the addition of real-time traffic data improved the CMAQ model's performance, especially for the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations. According to sensitivity tests for total and vehicle emissions in the Greater Taipei Area, vehicle emissions contributed to >90% of CO, 80% of nitrogen oxides (NOx), and approximately 50% of PM2.5 in the downtown areas of Taipei. The vehicle emissions contribution was affected by both vehicle emissions and meteorological conditions. The connection between the surveillance camera data, vehicle emissions, and regional air quality models in this study can also be used to explore the impact of special events (e.g., long weekends and COVID-19 lockdowns) on air quality.
ABSTRACT
Starting in January 2020, the novel coronavirus, now known as acute respiratory syndrome coronavirus (SARS-CoV-2) and the disease that it causes (COVID-19) has had significant impacts on human health, the environment, and the economy globally. The rapid lockdown that occurred as well as its well documented timing allows for an unprecedented opportunity to examine the impact of air pollution from densely populated regions has on adjacent and pristine environments. Here, we use in situ and satellite observations to show that there was a step function decrease in two key indicators of air quality, nitrogen dioxide and airborne particulates, in locations within the Indo-Gangetic Plan (IGP) as a result of the Spring 2020 lockdown. Based on anomaly patterns, we find a dipole response with a statistically significant reduction in air pollution along the western IGP and Himalaya and an increase in air pollution in the eastern IGP and Himalaya. We show that spatial variability in the reductions in economic activity across northern India and the adjoining countries of Nepal, Pakistan, and Bangladesh contributed to this dipole as did a persistent atmospheric circulation anomaly across the region during the lockdown.
ABSTRACT
The COVID-19 pandemic has prompted governments around the world to impose mitigation strategies of unprecedented scales, typically involving some form of restrictions on social activities and transportation. The South Korean government has been recommending a collection of guidelines now known as social distancing, leading to reduced human activities. This study analyzes changes in the concentrations of fine particulate matter (PM2.5) during the 30-day periods before and since the start of social distancing on 29 February 2020 using measurement data from air quality monitoring stations at various locations of the seven major cities of South Korea, namely, Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, and Ulsan. All seven cities experienced decreased levels of PM2.5 concentration by up to 25% and smaller fluctuations during the period of social distancing. Inter-city comparisons show that the PM2.5 concentration changes are positively correlated with the city-wide PM2.5 emission fractions for mobile sources and negatively correlated with the city-wide PM2.5 emission fractions for combustion and industrial process sources. In addition, the meteorological influences favorable for transboundary pollutant transport have weakened during the period under COVID-19 social distancing. Intra-city comparisons show that decreases in the intra-city variability of PM2.5 concentration were larger in coastal cities than in inland cities. Comparisons between the inter- and intra-city variabilities in the PM2.5 concentration changes under social distancing highlight the importance of taking into account intra-city variabilities in addition to inter-city variabilities.
ABSTRACT
The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.
ABSTRACT
The complete lockdown due to COVID-19 pandemic has contributed to the improvement of air quality across the countries particularly in developing countries including India. This study aims to assess the air quality by monitoring major atmospheric pollutants such as AOD, CO, PM2.5, NO2, O3 and SO2 in 15 major cities of India using Air Quality Zonal Modeling. The study is based on two different data sources; (a) grid data (MODIS- Terra, MERRA-2, OMI and AIRS, Global Modeling and Assimilation Office, NASA) and (b) ground monitoring station data provided by Central Pollution Control Board (CPCB) / State Pollution Control Board (SPCB). The remotely sensed data demonstrated that the concentration of PM2.5 has declined by 14%, about 30% of NO2 in million-plus cities, 2.06% CO, SO2 within the range of 5 to 60%, whereas the concentration of O3 has increased by 1 to 3% in majority of cities compared with pre lockdown. On the other hand, CPCB/SPCB data showed more than 40% decrease in PM2.5 and 47% decrease in PM10 in north Indian cities, more than 35% decrease in NO2 in metropolitan cities, more than 85% decrease in SO2 in Chennai and Nagpur and more than 17% increase in O3 in five cities amid 43 days pandemic lockdown. The restrictions of anthropogenic activities have substantial effect on the emission of primary atmospheric pollutants.
ABSTRACT
The implementation of confinement and physical distancing measures to restrict people's activities and transit in the midst of the COVID-19 pandemic allowed us to study how these measures affect the air quality in urban areas with high pollution rates, such as Santiago, Chile. A comparative study between the concentrations of PM10, PM2.5, NOx, CO, and O3 during the months of March to May 2020 and the corresponding concentrations during the same period in 2017-2019 is presented. A combination of surface measurements from the air quality monitoring network of the city, remote satellite measurements, and simulations of traffic activity and road transport emissions allowed us to quantify the change in the average concentrations of each pollutant. Average relative changes of traffic emissions (between 61% and 68%) implied statistically significant concentrations reductions of 54%, 13%, and 11% for NOx, CO, and PM2.5, respectively, during the pandemic period compared to historical period. In contrast, the average concentration of O3 increased by 63% during 2020 compared to 2017-2019. The nonlinear response observed in the pollution levels can be attributed to the changes in the vehicular emission patterns during the pandemic and to the role of other sources such as residential emissions or secondary PM.