Avoiding high ozone pollution in Delhi, India.

Surface ozone is a major pollutant threatening public health, agricultural production and natural ecosystems. While measures to improve air quality in megacities such as Delhi are typically aimed at reducing levels of particulate matter (PM), ozone could become a greater threat if these measures focus on PM alone, as some air pollution mitigation steps can actually lead to an increase in surface ozone. A better understanding of the factors controlling ozone production in Delhi and the impact that PM mitigation measures have on ozone is therefore critical for improving air quality. Here, we combine in situ observations and model analysis to investigate the impact of PM reduction on the non-linear relationship between volatile organic compounds (VOC), nitrogen oxides (NOx) and ozone. In situ measurements of NOx, VOC, and ozone were conducted in Delhi during the APHH-India programme in summer (June) and winter (November) 2018. We observed hourly averaged ozone concentrations in the city of up to 100 ppbv in both seasons. We performed sensitivity simulations with a chemical box model to explore the impacts of PM on the non-linear VOC-NOx-ozone relationship in each season through its effect on aerosol optical depth (AOD). We find that ozone production is limited by VOC in both seasons, and is particularly sensitive to solar radiation in winter. Reducing NOx alone increases ozone, such that a 50% reduction in NOx emissions leads to 10-50% increase in surface ozone. In contrast, reducing VOC emissions can reduce ozone efficiently, such that a 50% reduction in VOC emissions leads to ∼60% reduction in ozone. Reducing PM alone also increases ozone, especially in winter, by reducing its dimming effects on photolysis, such that a 50% reduction in AOD can increase ozone by 25% and it also enhances VOC-limitation. Our results highlight the importance of reducing VOC emissions alongside PM to limit ozone pollution, as well as benefitting control of PM pollution through reducing secondary organic aerosol. This will greatly benefit the health of citizens and the local ecosystem in Delhi, and could have broader application for other megacities characterized by severe PM pollution and VOC-limited ozone production.

PM2.5 concentrations in London for 2008--a modeling analysis of contributions from road traffic.

UNLABELLED: We report on the analysis of contributions from road traffic emissions to fine particulate matter (PM2.5) concentrations within London for 2008 with the OSCAR Air Quality Assessment System. A spatiotemporal evaluation of the OSCAR system has been conducted with measurements from the London air quality network (LAQN). For the predicted and measured hourly time series of concentrations at 18 sites in London, the medians of correlation, mean absolute error, index of agreement, and factor of two (FAC2) of all stations were 0.80, 4.1 microg/m3, 0.86, and 74%, respectively. Spatial evaluation of modeled and observed annual mean concentrations also showed a fairly good agreement, with all the values falling within the FAC2 range. According to model predictions, the urban increment (including the contributions from urban traffic and other urban sources) was evaluated to be on the average 18%, 33%, 39%, and 43% of the total PM2.5 in suburban environments, in the urban background, near roads, and near busy roads, respectively. However, the highest values of the urban traffic increment can be around 50% of the total PM2.5 concentrations near motorways and major roads. The total concentrations (including regional background, and the contributions from urban traffic and other urban sources) can therefore be almost three times the regional background. The total urban increment close to busy roads was around 7-8 microg/m3, in which the estimated traffic contribution is more than 2 microg/m3. On the average, urban traffic contributes approximately 1 microg/m3 of PM2.5 to the urban background across London. According to modeling, approximately two-thirds of the traffic increment originated from exhaust emissions and most of the rest was due to brake and tire wear. IMPLICATIONS: The urban increment and traffic contribution to the total PM2.5 are significant and spatially heterogeneous across London. The highly heterogeneous distribution of PM2.5 hence requires detailed modeling studies to be carried out at high spatial resolution, which can be particularly important for exposure and health impact assessment. This type of information can be used to quantify health impacts resulting from specific sources of PM2.5 such as traffic emissions, to aid city and national decision makers when formulating pollution control strategies.

Temperature projections and heatwave attribution scenarios over India: A systematic review.

The average global temperature is rising due to anthropogenic emissions. Hence, a systematic approach was used to examine the projected impacts of rising global temperatures on heatwaves in India and provide insights into mitigation and adaptation strategies. With over 24,000 deaths attributed to heatwaves from 1992 to 2015, there is an urgent need to understand India's vulnerabilities and prepare adaptive strategies under various emission scenarios.This situation is predicted to worsen as heatwaves become more frequent, intense, and long-lasting. Severe heatwaves can exacerbate chronic health conditions, vector-borne diseases, air pollution, droughts and other socio-economic pressures causing higher mortality and morbidity. Heatwaves with severe consequences have increased and are expected to become more frequent in Indian climatic and geographical conditions. As per the future projection studies, the temperature could rise ±1.2° C to ±3.5° C and will start reducing by the end of 2050. The study also provides data from the research that employs climatic models and statistical approaches for a more precise characterization of heat extremes and improved projections. Also, the study appraises the past, present and future heatwave trend projections. Most of these studies compute future projections using the Coupled Model Intercomparison Project (CMIP5) models and Representative Concentration Pathway (RCP). Limited systematic reports have been found using CMIP6, whereas the best-suited and widely used method was the RCP8.5. The study findings will aid in identifying the zones most susceptible to heatwave risk and provide actionable projections for policymakers to examine the existing evidence for developing proper planning and policy formulation, considering the future climate and temperature projections.

Exposure to ambient air pollution and elevated blood levels of gamma-glutamyl transferase in a large Austrian cohort.

Gamma glutamyl transferase (GGT) is related to oxidative stress and an indicator for liver damage. We investigated the association between air pollution and GGT in a large Austrian cohort (N = 116,109) to better understand how air pollution affects human health. Data come from voluntary prevention visits that were routinely collected within the Vorarlberg Health Monitoring and Prevention Program (VHM&PP). Recruitment was ongoing from 1985 to 2005. Blood was drawn and GGT measured centralized in two laboratories. Land use regression models were applied to estimate individuals' exposure at their home address for particulate matter (PM) with a diameter of <2.5 µm (PM2.5), <10 µm (PM10), fraction between 10 µm and 2.5 µm (PMcoarse), as well as PM2.5 absorbance (PM2.5abs), NO2, NOx and eight components of PM. Linear regression models, adjusting for relevant individual and community-level confounders were calculated. The study population was 56 % female with a mean age of 42 years and mean GGT was 19.0 units. Individual PM2.5 and NO2 exposures were essentially below European limit values of 25 and 40 µg/m3, respectively, with means of 13.58 µg/m3 for PM2.5 and 19.93 µg/m3 for NO2. Positive associations were observed for PM2.5, PM10, PM2.5abs, NO2, NOx, and Cu, K, S in PM2.5 and PM10 fractions and Zn mainly in PM2.5 fraction. The strongest association per interquartile range observed was an increase of serum GGT concentration by 1.40 % (95 %-CI: 0.85 %; 1.95 %) per 45.7 ng/m3 S in PM2.5. Associations were robust to adjustments for other biomarkers, in two-pollutant models and the subset with a stable residential history. We found that long-term exposure to air pollution (PM2.5, PM10, PM2.5abs, NO2, NOx) as well as certain elements, were positively associated with baseline GGT levels. The elements associated suggest a role of traffic emissions, long range transport and wood burning.

Predictions of U.K. regulated power station contributions to regional air pollution and deposition: a model comparison exercise.

Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics-Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

An approach to predict population exposure to ambient air PM2.5 concentrations and its dependence on population activity for the megacity London.

A comprehensive modelling approach has been developed to predict population exposure to the ambient air PM2.5 concentrations in different microenvironments in London. The modelling approach integrates air pollution dispersion and exposure assessment, including treatment of the locations and time activity of the population in three microenvironments, namely, residential, work and transport, based on national demographic information. The approach also includes differences between urban centre and suburban areas of London by taking account of the population movements and the infiltration of PM2.5 from outdoor to indoor. The approach is tested comprehensively by modelling ambient air concentrations of PM2.5 at street scale for the year 2008, including both regional and urban contributions. Model analysis of the exposure in the three microenvironments shows that most of the total exposure, 85%, occurred at home and work microenvironments and 15% in the transport microenvironment. However, the annual population weighted mean (PWM) concentrations of PM2.5 for London in transport microenvironments were almost twice as high (corresponding to 13-20 µg/m3) as those for home and work environments (7-12 µg/m3). Analysis has shown that the PWM PM2.5 concentrations in central London were almost 20% higher than in the surrounding suburban areas. Moreover, the population exposure in the central London per unit area was almost three times higher than that in suburban regions. The exposure resulting from all activities, including outdoor to indoor infiltration, was about 20% higher, when compared with the corresponding value obtained assuming inside home exposure for all times. The exposure assessment methodology used in this study predicted approximately over one quarter (-28%) lower population exposure, compared with using simply outdoor concentrations at residential locations. An important implication of this study is that for estimating population exposure, one needs to consider the population movements, and the infiltration of pollution from outdoors to indoors.

Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown.

Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overallsense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluaion methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.