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Long-term changes in black carbon aerosols and their health effects in rural India during the past two decades (2000-2019)
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The Earth’s surface temperatures have increased significantly since the beginning of industrialisation. The high emissions of greenhouse gases have contributed to global warming and climate change, which is expected to continue. Here, we investigate the long-term trends of surface temperature in India using surface, satellite and reanalysis data for the period of 1980–2020, and assess the influence of geophysical drivers on temperature change using the causal discovery. The highest mean surface temperatures are observed in the southern India and the lowest in Himalaya in accordance with the incidence of solar radiation. Causal discovery reveals the relative influence of atmospheric processes, aerosols and specific humidity on surface temperature. We observe a positive trend in temperature during pre-monsoon (0.1–0.3 °C dec−1) and post-monsoon (0.2–0.4 °C dec−1) seasons in the northwest, northeast and north-central India. The analysis exposes high annual (0.22 ± 0.14 °C dec−1) and monsoon (0.24 ± 0.08 °C dec−1) warming in the northeast India. Post-monsoon season shows a positive trend in the entire India, where the highest value is estimated for the western Himalaya (0.2–0.5 °C dec−1) and northeast India (0.1–0.4 °C dec−1). Analyses with the Coupled Model Intercomparison Project 6 (CMIP6) results show that temperature can increase up to 1.1–5.1 °C by year 2100 under the Shared Socioeconomic Pathways (SSP5)–8.5 scenario. The increasing trend of temperature in India is a big concern, which calls for adaption and mitigation measures to alleviate adverse effects of accelerated warming and regional climate change.
Crop residue burning contributes to poor air quality and imposes a health burden on India. Despite government bans and other interventions, this practice remains widespread. Here we estimate the impact of changes in agricultural emissions on air quality across India and quantify the potential benefit of district-level actions using an adjoint modeling approach. From 2003 to 2019, we find that agricultural residue burning caused 44,000–98,000 particulate matter exposure-related premature deaths annually, of which Punjab, Haryana, and Uttar Pradesh contribute 67–90%. Due to a combination of relatively high downwind population density, agricultural output, and cultivation of residue-intensive crops, six districts in Punjab alone contribute to 40% of India-wide annual air quality impacts from residue burning. Burning two hours earlier in Punjab alone could avert premature deaths up to 9600 (95% CI: 8000–11,000) each year, valued at 3.2 (95% CI: 0.49–7.3) billion US dollars. Our findings support the use of targeted and potentially low-cost interventions to mitigate crop residue burning in India, pending further research regarding cost-effectiveness and feasibility.
India is a country with more than 67% of its population (947 million) residing in rural areas and 33% in urban areas (472 million) as of 2020. Therefore, health of the people living in rural India is very important for its future development plans, economy and growth. Here, we analyse the rural air quality using satellite measurements of NO2 in India, as the sources of NO2 are well connected to the industrial and economic uplift of a nation. Our analyses for the rural regions show distinct seasonal changes with the highest value (2.0 × 1015 molecules per cm2) in winter and the lowest in monsoon (1.5 × 1015 molecules per cm2) seasons. About 41% of the total NO2 pollution in India is from its rural sources, but 59% of the urban sources were focused in the past studies. In addition, around 45% of the rural NO2 pollution is due to road transport, whereas more than 90% of it in urban India comes from the power sector. Our assessment shows that the NO2 exposure in rural regions is as serious as that in urban areas, indicating the need for more effective reduction of population exposure and protection of public health. Henceforth, this study reveals that rural India is gradually getting polluted from its nearby regions as well as from the new sources within. This is a big concern for the public health of the large rural population of India.
Every year, forest fires and harvest harnessing produce atmospheric pollution in October and November over the Indo-Gangetic Plain (IGP). The fire count data (MODIS) shows a decreasing/increasing trend of fire counts in all confidence ranges in October/November over Northern India. There is a widespread increase in fires with a confidence level above 60 to 80% over the whole Northern Indian region. The Aerosol Optical Index (AOD) also shows an increase with values > 0.7 over the northwestern and IGP regions. There have been some startling results over the lower IGP belt, where there has been increasing trend in AOD during October ~56% and during November, the increase was by a whopping ~116%. However, in November, a slight turning of the winds towards central India might be transporting the AOD towards the central Indian region. Hence, during November, it is inferred that due to the low wind speed over the lower IGP belt and increased fires, the AODs in the polluted air tend to hover for a long time. During recent years from 2010, the winds have become stronger, indicating more transport of AOD is occurring over the lower IGP belt as compared to previous years till 2009, especially in October.
We estimated mortality and economic loss attributable to PM 2·5 air pollution exposure in 429 counties of Iran in 2018. Ambient PM 2.5 -related deaths were estimated using the Global Exposure Mortality Model (GEMM). According to the ground-monitored and satellite-based PM 2.5 data, the annual mean population-weighted PM 2·5 concentrations for Iran were 30.1 and 38.6 μg m ⁻³ , respectively. We estimated that long-term exposure to ambient PM 2.5 contributed to 49,303 (95% confidence interval (CI) 40,914–57,379) deaths in adults ≥ 25 yr. from all-natural causes based on ground monitored data and 58,873 (95% CI 49,024–68,287) deaths using satellite-based models for PM 2.5 . The crude death rate and the age-standardized death rate per 100,000 population for age group ≥ 25 year due to ground-monitored PM 2.5 data versus satellite-based exposure estimates was 97 (95% CI 81–113) versus 116 (95% CI 97–135) and 125 (95% CI 104–145) versus 149 (95% CI 124–173), respectively. For ground-monitored and satellite-based PM 2.5 data, the economic loss attributable to ambient PM 2.5 -total mortality was approximately 10,713 (95% CI 8890–12,467) and 12,792.1 (95% CI 10,652.0–14,837.6) million USD, equivalent to nearly 3.7% (95% CI 3.06–4.29) and 4.3% (95% CI 3.6–4.5.0) of the total gross domestic product in Iran in 2018.
A large discrepancy between simulated and observed black carbon (BC) surface concentrations over the densely populated Indo-Gangetic plain (IGP) has so far limited our ability to assess the magnitude of BC health impacts in terms of population exposure, morbidity, and mortality. We evaluate these impacts using an integrated modeling framework, including successfully predicted BC concentrations. Population exposure to BC is notable, with more than 60 million people identified as living in hotspots of BC concentration (wintertime mean, >20 μg m ⁻³ ). The attributable fraction of the total cardiovascular disease mortality (CVM) burden to BC exposures is 62% for the megacity. The semiurban area comprised about 49% of the total BC-attributable CVM burden over the IGP. More than 400,000 lives can potentially be saved from CVM annually by implementing prioritized emission reduction from the combustion of domestic biofuel in the semiurban area, diesel oil in transportation, and coal in thermal power plant and brick kiln industries in megacities.
Significance
The pollution in major cities in India is immense, and they are in the news often. In general, air pollution is viewed as an urban problem. Here, we show that outdoor air pollution due to particulate matter is just as harmful in nonurban (rural) areas as in urban areas in India based on satellite data and calculated health impacts. This finding has a far-reaching impact on how pollution is viewed in India and other developing countries. It is also very timely given that India, and countries like India, are turning attention to addressing air pollution, and they need to be aware of this vast issue.
Wind power, a rapidly growing alternative energy source, has been threatened by reductions in global average surface wind speed, which have been occurring over land since the 1980s, a phenomenon known as global terrestrial stilling. Here, we use wind data from in situ stations worldwide to show that the stilling reversed around 2010 and that global wind speeds over land have recovered. We illustrate that decadal-scale variations of near-surface wind are probably determined by internal decadal ocean–atmosphere oscillations, rather than by vegetation growth and/or urbanization as hypothesized previously. The strengthening has increased potential wind energy by 17 ± 2% for 2010 to 2017, boosting the US wind power capacity factor by ~2.5% and explains half the increase in the US wind capacity factor since 2010. In the longer term, the use of ocean–atmosphere oscillations to anticipate future wind speeds could allow optimization of turbines for expected speeds during their productive life spans.
We examine long-term trends in the near-surface black carbon mass concentration, using multiyear primary data obtained from a dense network (ARFINET) of observatories over the Indian region. We report for the first time the statistically significant decreasing trend in black carbon mass concentration, based on primary data from this region, at an average rate of ~242 ± 53 ng · m ⁻³ · year ⁻¹ during the period 2007–2016. This finding contrasts with the generally increasing trend in the columnar aerosol optical depth, reported earlier, and the steadily increasing trend in anthropogenic activities over this region. The roles of different possible mechanisms, including possible changes in the vertical redistribution of aerosols, are discussed. Over the period 2007–2015, a significant though weak, increasing trend is seen in the contribution from aerosols above 1 km to the columnar aerosol optical depth. These observations imply possible long-term climate consequences.
The new version of the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2) compiles gaseous and particulate air pollutant emissions, making use of the same anthropogenic sectors, time period (1970–2012), and international activity data that is used for estimating GHG emissions, as described in a companion paper (Janssens-Maenhout et al., 2017). All human activities, except large scale biomass burning and land use, land-use change, and forestry are included in the emissions calculation. The bottom-up compilation methodology of sector-specific emissions was applied consistently for all world countries, providing methodological transparency and comparability between countries. In addition to the activity data used to estimate GHG emissions, air pollutant emissions are determined by the process technology and end-of-pipe emission reduction abatements. Region-specific emission factors and abatement measures were selected from recent available scientific literature and reports. Compared to previous versions of EDGAR, the EDGAR v4.3.2 dataset covers all gaseous and particulate air pollutants, has extended time series (1970–2012), and has been evaluated with quality control and quality assurance (QC and QA) procedures both for the emission time series (e.g. particulate matter – PM – mass balance, gap-filling for missing data, the split-up of countries over time, few updates in the emission factors, etc.) and grid maps (full coverage of the world, complete mapping of EDGAR emissions with sector-specific proxies, etc.). This publication focuses on the gaseous air pollutants of CO, NOx, SO2, total non-methane volatile organic compounds (NMVOCs), NH3, and the aerosols PM10, PM2.5, black carbon (BC), and organic carbon (OC). Considering the 1970–2012 time period, global emissions of SO2 increased from 99 to 103 Mt, CO from 441 to 562 Mt, NOx from 68 to 122 Mt, NMVOC from 119 to 170 Mt, NH3 from 25 to 59 Mt, PM10 from 37 to 65 Mt, PM2.5 from 24 to 41 Mt, BC from 2.7 to 4.5 Mt, and OC from 9 to 11 Mt. We present the country-specific emission totals and analyze the larger emitting countries (including the European Union) to provide insights on major sector contributions. In addition, per capita and per GDP emissions and implied emission factors – the apparent emissions per unit of production or energy consumption – are presented. We find that the implied emission factors (EFs) are higher for low-income countries compared to high-income countries, but in both cases decrease from 1970 to 2012. The comparison with other global inventories, such as the Hemispheric Transport of Air Pollution Inventory (HTAP v2.2) and the Community Emission Data System (CEDS), reveals insights on the uncertainties as well as the impact of data revisions (e.g. activity data, emission factors, etc.). As an additional metric, we analyze the emission ratios of some pollutants to CO2 (e.g. CO/CO2, NOx/CO2, NOx/CO, and SO2/CO2) by sector, region, and time to identify any decoupling of air pollutant emissions from energy production activities and to demonstrate the potential of such ratios to compare to satellite-derived emission data. Gridded emissions are also made available for the 1970–2012 historic time series, disaggregated for 26 anthropogenic sectors using updated spatial proxies. The analysis of the evolution of hot spots over time allowed us to identify areas with growing emissions and where emissions should be constrained to improve global air quality (e.g. China, India, the Middle East, and some South American countries are often characterized by high emitting areas that are changing rapidly compared to Europe or the USA, where stable or decreasing emissions are evaluated). Sector- and component-specific contributions to grid-cell emissions may help the modelling and satellite communities to disaggregate atmospheric column amounts and concentrations into main emitting sectors. This work addresses not only the emission inventory and modelling communities, but also aims to broaden the usefulness of information available in a global emission inventory such as EDGAR to also include the measurement community. Data are publicly available online through the EDGAR website http://edgar.jrc.ec.europa.eu/overview.php?v=432_AP and registered under 10.2904/JRC_DATASET_EDGAR.
Continuous and high temporal resolution surface based measurements of Black Carbon (BC) concentrations have been carried out during 1st September 2014 to 30th April 2017 at Thiruvananthapuram (8.5°N, 76.9°E), a near equatorial tropical coastal station, in South India. High diurnal and seasonal variations of BC aerosols in relation to changes in the regional meteorological conditions are examined. The BC exhibited well defined diurnal cycle with a bimodal peak one in the morning (07.00–08.00 LST) and other in evening (21.00–23.00 LST). Relative humidity of 60–90% and 70–90% favours the BC mass concentration in daytime and nighttime respectively. BC mass concentration is considerably less for the relative humidity category of 90–100%. BC greater than 10 μg m⁻³ are mostly associated with low wind speed (less than 2 ms⁻¹). Hence the local BC emissions are not well mixed or transported to a larger distance in calm or low wind speed. In high wind speed local emissions are quickly and well mixed in the air. Coherence wavelet analysis between BC and rainfall shows periodicities in the synoptic scale of less than 10 days and in the between 16–32 days. This shows the importance of intra seasonal rainfall scavenging on BC concentration over the tropical region. Synoptic systems over the monsoon region shows high scavenging relation. The bivariate analysis pointed that the local emission from northeast sector contributes the black carbon aerosol to the observational site. HYSPLIT trajectory model reveals that the long range transport of airmass is through northwest India and Bay of Bengal in winter season.
Detecting causal associations in time series datasets is a key challenge for novel insights into complex dynamical systems such as the Earth system or the human brain. Interactions in high-dimensional dynamical systems often involve time-delays, nonlinearity, and strong autocorrelations. These present major challenges for causal discovery techniques such as Granger causality leading to low detection power, biases, and unreliable hypothesis tests. Here we introduce a reliable and fast method that outperforms current approaches in detection power and scales up to high-dimensional datasets. It overcomes detection biases, especially when strong autocorrelations are present, and allows ranking associations in large-scale analyses by their causal strength. We provide mathematical proofs, evaluate our method in extensive numerical experiments, and illustrate its capabilities in a large-scale analysis of the global surface-pressure system where we unravel spurious associations and find several potentially causal links that are difficult to detect with standard methods. The broadly applicable method promises to discover novel causal insights also in many other fields of science.
Vegetation and peatland fires cause poor air quality and thousands of premature deaths across densely populated regions in Equatorial Asia. Strong El-Niño and positive Indian Ocean Dipole conditions are associated with an increase in the frequency and intensity of wildfires in Indonesia and Borneo, enhancing population exposure to hazardous concentrations of smoke and air pollutants. Here we investigate the impact on air quality and population exposure of wildfires in Equatorial Asia during Fall 2015, which were the largest over the past two decades. We performed high-resolution simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emission product. The model captures the spatio-temporal variability of extreme pollution episodes relative to space- and ground-based observations and allows for identification of pollution sources and transport over Equatorial Asia. We calculate that high particulate matter concentrations from fires during Fall 2015 were responsible for persistent exposure of 69 million people to unhealthy air quality conditions. Short-term exposure to this pollution may have caused 11,880 (6,153–17,270) excess mortalities. Results from this research provide decision-relevant information to policy makers regarding the impact of land use changes and human driven deforestation on fire frequency and population exposure to degraded air quality.
Agricultural crop residue burning contribute towards the emission of greenhouse gases (CO 2 , N 2 O, CH 4), air pollutants (CO, NH 3 , NO x , SO 2 , NMHC, volatile organic compounds), particulates matter and smoke thereby posing threat to human health. In the present study a state-wise inventory of crop residue burnt in India and the air pollutants emitted was prepared using the InterGovernmental Panel on Climate Change (IPCC) national inventory preparation guidelines for the year 2008–09. Total amount of residue generated in 2008–09 was 620 Mt out of which ~15.9% residue was burnt on farm. Rice straw contributed 40% of the total residue burnt followed by wheat straw (22%) and sugarcane trash (20%). Burning of crop residues emitted 8.57 Mt of CO, 141.15 Mt of CO 2 , 0.037 Mt of SO x , 0.23 Mt of NO x , 0.12 Mt of NH 3 and 1.46 Mt NMVOC, 0.65 Mt of NMHC, 1.21 Mt of particulate matter for the year 2008–09. The variability of 21.46% in annual emission of air pollutants was observed from 1995 to 2009.
Some recent high-profile publications have suggested that immediately
reducing emissions of methane, black carbon and other short-lived
climate pollutants (SLCPs) may contribute substantially towards the goal
of limiting global warming to 2 °C above pre-industrial levels.
Although this literature acknowledges that action on long-lived climate
pollutants (LLCPs) such as CO2 is also required, it is not
always appreciated that SLCP emissions in any given decade only have a
significant impact on peak temperature under circumstances in which
CO2 emissions are falling. Immediate action on SLCPs might
potentially 'buy time' for adaptation by reducing near-term warming;
however early SLCP reductions, compared with reductions in a future
decade, do not buy time to delay reductions in CO2.
Black carbon aerosol plays a unique and important role in Earth's climate system. Black carbon is a type of carbonaceous material with a unique combination of physical properties. This assessment provides an evaluation of black-carbon climate forcing that is comprehensive in its inclusion of all known and relevant processes and that is quantitative in providing best estimates and uncertainties of the main forcing terms: direct solar absorption; influence on liquid, mixed phase, and ice clouds; and deposition on snow and ice. These effects are calculated with climate models, but when possible, they are evaluated with both microphysical measurements and field observations. Predominant sources are combustion related, namely, fossil fuels for transportation, solid fuels for industrial and residential uses, and open burning of biomass. Total global emissions of black carbon using bottom-up inventory methods are 7500 Gg yr(-1) in the year 2000 with an uncertainty range of 2000 to 29000. However, global atmospheric absorption attributable to black carbon is too low in many models and should be increased by a factor of almost 3. After this scaling, the best estimate for the industrial-era (1750 to 2005) direct radiative forcing of atmospheric black carbon is +0.71 W m(-2) with 90% uncertainty bounds of (+0.08, +1.27) W m(-2). Total direct forcing by all black carbon sources, without subtracting the preindustrial background, is estimated as +0.88 (+0.17, +1.48) W m(-2). Direct radiative forcing alone does not capture important rapid adjustment mechanisms. A framework is described and used for quantifying climate forcings, including rapid adjustments. The best estimate of industrial-era climate forcing of black carbon through all forcing mechanisms, including clouds and cryosphere forcing, is +1.1 W m(-2) with 90% uncertainty bounds of +0.17 to +2.1 W m(-2). Thus, there is a very high probability that black carbon emissions, independent of co-emitted species, have a positive forcing and warm the climate. We estimate that black carbon, with a total climate forcing of +1.1 W m(-2), is the second most important human emission in terms of its climate forcing in the present-day atmosphere; only carbon dioxide is estimated to have a greater forcing. Sources that emit black carbon also emit other short-lived species that may either cool or warm climate. Climate forcings from co-emitted species are estimated and used in the framework described herein. When the principal effects of short-lived co-emissions, including cooling agents such as sulfur dioxide, are included in net forcing, energy-related sources (fossil fuel and biofuel) have an industrial-era climate forcing of +0.22 (-0.50 to +1.08) W m(-2) during the first year after emission. For a few of these sources, such as diesel engines and possibly residential biofuels, warming is strong enough that eliminating all short-lived emissions from these sources would reduce net climate forcing (i.e., produce cooling). When open burning emissions, which emit high levels of organic matter, are included in the total, the best estimate of net industrial-era climate forcing by all short-lived species from black-carbon-rich sources becomes slightly negative (-0.06 W m(-2) with 90% uncertainty bounds of -1.45 to +1.29 W m(-2)). The uncertainties in net climate forcing from black-carbon-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both black carbon and co-emitted organic carbon. In prioritizing potential black-carbon mitigation actions, non-science factors, such as technical feasibility, costs, policy design, and implementation feasibility play important roles. The major sources of black carbon are presently in different stages with regard to the feasibility for near-term mitigation. This assessment, by evaluating the large number and complexity of the associated physical and radiative processes in black-carbon climate forcing, sets a baseline from which to improve future climate forcing estimates.
Fifteen years of “equivalent” black carbon (EBC) measurements (derived from aethalometer measurements of light absorption) made at Alert in Nunavut, Canada, and Point Barrow in Alaska, United States, were compared for the long-term trends and seasonal cycle. Over the 15-year period from 1989 to 2003, the results revealed a downward trend in EBC concentrations by as much as 54% at Alert and 27% at Barrow for the all-year data, by 49% at Alert and 33% at Barrow for the winter data, and by 53% at Alert for the summer. It was difficult to quantify if there was a decline during the summer for Barrow since there was no clear trend. The difference in trends might be related to changes in circulation in the Arctic, variable source contribution, and/or scavenging of particles. The results revealed that EBC concentrations were 40% higher during the positive phase of the North Atlantic Oscillation than during the negative phase. The source contributions at the two sites were determined by using trajectory analysis techniques, which revealed that Alert came under the influence of Siberia/Europe transport while Barrow showed influence from Siberian and Pacific/Asian transport.
During the winter and spring the North American Arctic is impacted by anthropogenic black carbon (BC) in ``Arctic Haze'' pollution from sources mainly located in Europe and Russia. This black carbon, while suspended in the atmosphere and in surface snow, has a significant effect on radiative forcing of the Arctic atmosphere. Routine ground-level observations of aerosol black carbon by optical absorption have been made at a Canadian Arctic location, Alert (82.5°N, 62.5°W), Nunavut since 1989. A 3-year intensive study was conducted to compare BC obtained by the thermal analysis and optical absorption methods, so that the seasonal variations in the ``operational'' absorption cross sections of the aerosol could be determined. A time series analysis indicated that black carbon concentrations undergo a strong seasonal variation superimposed upon a long-term trend. The latter shows a decrease of about 55% in BC concentrations between 1989 and 2002 at Alert. Factors responsible for these trends such as changes in emissions and atmospheric transport support the hypothesis that BC emissions from the former USSR are mostly responsible for the observed decreasing trend. Transport from other sectors such as North America and Europe are not as prevalent at Alert.
In many large cities of Europe standard air quality limit values of particulate matter (PM) are exceeded. Emissions from road traffic and biomass burning are frequently reported to be the major causes. As a consequence of these exceedances a large number of air quality plans, most of them focusing on traffic emissions reductions, have been implemented in the last decade. In spite of this implementation, a number of cities did not record a decrease of PM levels. Thus, is the efficiency of air quality plans overestimated? Do the road traffic emissions contribute less than expected to ambient air PM levels in urban areas? Or do we need a more specific metric to evaluate the impact of the above emissions on the levels of urban aerosols?
This study shows the results of the interpretation of the 2009 variability of levels of PM, Black Carbon (BC), aerosol number concentration (N) and a number of gaseous pollutants in seven selected urban areas covering road traffic, urban background, urban-industrial, and urban-shipping environments from southern, central and northern Europe.
The results showed that variations of PM and N levels do not always reflect the variation of the impact of road traffic emissions on urban aerosols. However, BC levels vary proportionally with those of traffic related gaseous pollutants, such as CO, NO2 and NO. Due to this high correlation, one may suppose that monitoring the levels of these gaseous pollutants would be enough to extrapolate exposure to traffic-derived BC levels. However, the BC/CO, BC/NO2 and BC/NO ratios vary widely among the cities studied, as a function of distance to traffic emissions, vehicle fleet composition and the influence of other emission sources such as biomass burning. Thus, levels of BC should be measured at air quality monitoring sites.
During morning traffic rush hours, a narrow variation in the N/BC ratio was evidenced, but a wide variation of this ratio was determined for the noon period. Although in central and northern Europe N and BC levels tend to vary simultaneously, not only during the traffic rush hours but also during the whole day, in urban background stations in southern Europe maximum N levels coinciding with minimum BC levels are recorded at midday in all seasons. These N maxima recorded in southern European urban background environments are attributed to midday nucleation episodes occurring when gaseous pollutants are diluted and maximum insolation and O3 levels occur. The occurrence of SO2 peaks may also contribute to the occurrence of midday nucleation bursts in specific industrial or shipping-influenced areas, although at several central European sites similar levels of SO2 are recorded without yielding nucleation episodes.
Accordingly, it is clearly evidenced that N variability in different European urban environments is not equally influenced by the same emission sources and atmospheric processes. We conclude that N variability does not always reflect the impact of road traffic on air quality, whereas BC is a more consistent tracer of such an influence. However, N should be measured since ultrafine particles (<100 nm) may have large impacts on human health.
The combination of PM10 and BC monitoring in urban areas potentially constitutes a useful approach for air quality monitoring. BC is mostly governed by vehicle exhaust emissions, while PM10 concentrations at these sites are also governed by non-exhaust particulate emissions resuspended by traffic, by midday atmospheric dilution and by other non-traffic emissions.
Tropospheric ozone and black carbon (BC) contribute to both degraded air quality and global warming. We considered ~400 emission
control measures to reduce these pollutants by using current technology and experience. We identified 14 measures targeting
methane and BC emissions that reduce projected global mean warming ~0.5°C by 2050. This strategy avoids 0.7 to 4.7 million
annual premature deaths from outdoor air pollution and increases annual crop yields by 30 to 135 million metric tons due to
ozone reductions in 2030 and beyond. Benefits of methane emissions reductions are valued at 5000 per metric ton,
which is well above typical marginal abatement costs (less than $250). The selected controls target different sources and
influence climate on shorter time scales than those of carbon dioxide–reduction measures. Implementing both substantially
reduces the risks of crossing the 2°C threshold.
Surface winds have declined in China, the Netherlands, the Czech Republic, the United States and Australia over the past few decades. The precise cause of the stilling is uncertain. Here, we analyse the extent and potential cause of changes in surface wind speeds over the northern mid-latitudes between 1979 and 2008, using data from 822 surface weather stations. We show that surface wind speeds have declined by 5g-15% over almost all continental areas in the northern mid-latitudes, and that strong winds have slowed faster than weak winds. In contrast, upper-air winds calculated from sea-level pressure gradients, and winds from weather reanalyses, exhibited no such trend. Changes in atmospheric circulation that are captured by reanalysis data explain 10g-50% of the surface wind slowdown. In addition, mesoscale model simulations suggest that an increase in surface roughnessg-the magnitude of which is estimated from increases in biomass and land-use change in Eurasiag-could explain between 25 and 60% of the stilling. Moreover, regions of pronounced stilling generally coincided with regions where biomass has increased over the past 30 years, supporting the role of vegetation increases in wind slowdown.
Current air quality standards for particulate matter (PM) use the PM mass concentration [PM with aerodynamic diameters ≤ 10 μm (PM(10)) or ≤ 2.5 μm (PM(2.5))] as a metric. It has been suggested that particles from combustion sources are more relevant to human health than are particles from other sources, but the impact of policies directed at reducing PM from combustion processes is usually relatively small when effects are estimated for a reduction in the total mass concentration.
We evaluated the value of black carbon particles (BCP) as an additional indicator in air quality management.
We performed a systematic review and meta-analysis of health effects of BCP compared with PM mass based on data from time-series studies and cohort studies that measured both exposures. We compared the potential health benefits of a hypothetical traffic abatement measure, using near-roadway concentration increments of BCP and PM(2.5) based on data from prior studies.
Estimated health effects of a 1-μg/m3 increase in exposure were greater for BCP than for PM(10) or PM(2.5), but estimated effects of an interquartile range increase were similar. Two-pollutant models in time-series studies suggested that the effect of BCP was more robust than the effect of PM mass. The estimated increase in life expectancy associated with a hypothetical traffic abatement measure was four to nine times higher when expressed in BCP compared with an equivalent change in PM(2.5) mass.
BCP is a valuable additional air quality indicator to evaluate the health risks of air quality dominated by primary combustion particles.
Several studies have reported associations between long-term exposure to ambient fine particulate matter (PM) and cardiovascular mortality. However, the health impacts of long-term exposure to specific constituents of PM(2.5) (PM with aerodynamic diameter < or = 2.5 microm) have not been explored.
We used data from the California Teachers Study, a prospective cohort of active and former female public school professionals. We developed estimates of long-term exposures to PM(2.5) and several of its constituents, including elemental carbon, organic carbon (OC), sulfates, nitrates, iron, potassium, silicon, and zinc. Monthly averages of exposure were created using pollution data from June 2002 through July 2007. We included participants whose residential addresses were within 8 and 30 km of a monitor collecting PM(2.5) constituent data. Hazard ratios (HRs) were estimated for long-term exposure for mortality from all nontraumatic causes, cardiopulmonary disease, ischemic heart disease (IHD), and pulmonary disease.
Approximately 45,000 women with 2,600 deaths lived within 30 km of a monitor. We observed associations of all-cause, cardiopulmonary, and IHD mortality with PM(2.5) mass and each of its measured constituents, and between pulmonary mortality and several constituents. For example, for cardiopulmonary mortality, HRs for interquartile ranges of PM(2.5), OC, and sulfates were 1.55 [95% confidence interval (CI), 1.431.69], 1.80 (95% CI, 1.681.93), and 1.79 (95% CI, 1.582.03), respectively. Subsequent analyses indicated that, of the constituents analyzed, OC and sulfates had the strongest associations with all four outcomes.
Long-term exposures to PM(2.5) and several of its constituents were associated with increased risks of all-cause and cardiopulmonary mortality in this cohort. Constituents derived from combustion of fossil fuel (including diesel), as well as those of crustal origin, were associated with some of the greatest risks. These results provide additional evidence that reduction of ambient PM(2.5) may provide significant public health benefits.
We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.
Population-based studies have estimated health risks of short-term exposure to fine particles using mass of PM(2.5) (particulate matter <or= 2.5 microm in aerodynamic diameter) as the indicator. Evidence regarding the toxicity of the chemical components of the PM(2.5) mixture is limited.
In this study we investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of PM(2.5) in the United States.
We used a national database comprising daily data for 2000-2006 on emergency hospital admissions for cardiovascular and respiratory outcomes, ambient levels of major PM(2.5) chemical components [sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), and sodium and ammonium ions], and weather. Using Bayesian hierarchical statistical models, we estimated the associations between daily levels of PM(2.5) components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees (>or= 65 years of age).
In multiple-pollutant models that adjust for the levels of other pollutants, an interquartile range (IQR) increase in EC was associated with a 0.80% [95% posterior interval (PI), 0.34-1.27%] increase in risk of same-day cardiovascular admissions, and an IQR increase in OCM was associated with a 1.01% (95% PI, 0.04-1.98%) increase in risk of respiratory admissions on the same day. Other components were not associated with cardiovascular or respiratory hospital admissions in multiple-pollutant models.
Ambient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of PM(2.5).
A large body of epidemiologic literature has found an association of increased fine particulate air pollution (PM2.5) with acute and chronic mortality. The effect of improvements in particle exposure is less clear.
Earlier analysis of the Harvard Six Cities adult cohort study showed an association between long-term ambient PM2.5 and mortality between enrollment in the mid-1970s and follow-up until 1990. We extended mortality follow-up for 8 yr in a period of reduced air pollution concentrations.
Annual city-specific PM2.5 concentrations were measured between 1979 and 1988, and estimated for later years from publicly available data. Exposure was defined as (1) city-specific mean PM2.5 during the two follow-up periods, (2) mean PM2.5 in the first period and change between these periods, (3) overall mean PM2.5 across the entire follow-up, and (4) year-specific mean PM2.5. Mortality rate ratios were estimated with Cox proportional hazards regression controlling for individual risk factors.
We found an increase in overall mortality associated with each 10 microg/m3 increase in PM2.5 modeled either as the overall mean (rate ratio [RR], 1.16; 95% confidence interval [CI], 1.07-1.26) or as exposure in the year of death (RR, 1.14; 95% CI, 1.06-1.22). PM2.5 exposure was associated with lung cancer (RR, 1.27; 95% CI, 0.96-1.69) and cardiovascular deaths (RR, 1.28; 95% CI, 1.13-1.44). Improved overall mortality was associated with decreased mean PM2.5 (10 microg/m3) between periods (RR, 0.73; 95% CI, 0.57-0.95).
Total, cardiovascular, and lung cancer mortality were each positively associated with ambient PM2.5 concentrations. Reduced PM2.5 concentrations were associated with reduced mortality risk.
Several epidemiologic studies provide evidence of an association between daily mortality and particulate matter < 2.5 pm in diameter (PM2.5). Little is known, however, about the relative effects of PM2.5 constituents. We examined associations between 19 PM2.5 components and daily mortality in six California counties.
We obtained daily data from 2000 to 2003 on mortality and PM2.5 mass and components, including elemental and organic carbon (EC and OC), nitrates, sulfates, and various metals. We examined associations of PM2.5 and its constituents with daily counts of several mortality categories: all-cause, cardiovascular, respiratory, and mortality age > 65 years. Poisson regressions incorporating natural splines were used to control for time-varying covariates. Effect estimates were determined for each component in each county and then combined using a random-effects model.
PM2.5 mass and several constituents were associated with multiple mortality categories, especially cardiovascular deaths. For example, for a 3-day lag, the latter increased by 1.6, 2.1, 1.6, and 1.5% for PM2.5, EC, OC, and nitrates based on interquartile ranges of 14.6, 0.8, 4.6, and 5.5 pg/m(3), respectively. Stronger associations were observed between mortality and additional pollutants, including sulfates and several metals, during the cool season.
This multicounty analysis adds to the growing body of evidence linking PM2.5 with mortality and indicates that excess risks may vary among specific PM2.5 components. Therefore, the use of regression coefficients based on PM2.5 mass may underestimate associations with some PM2.5 components. Also, our findings support the hypothesis that combustion-associated pollutants are particularly important in California.
The COVID-19 lockdown (LD) provided a unique opportunity to examine the changes in regional and global air quality. Changes in the atmospheric carbon monoxide (CO) concentrations during LD warrant a thorough analysis as CO is a major air pollutant that affects human health, ecosystem and climate. Our analysis reveals a decrease of 5-10% in the CO concentrations during LD (April-May 2020) compared to the pre-lockdown (PreLD, March 2020) periods in regions with high anthropogenic activity, such as East China (EC), Indo-Gangetic Plain (IGP), North America, parts of Europe and Russia. However, this reduction did not occur in the regions of frequent and intense wildfires and agricultural waste burning (AWB). We find high heterogeneity in the CO column distributions, from regional to city scales during the LD period. To determine the sources of CO emissions during LD, we examined the ratios of nitrogen dioxide (NO2), sulfur dioxide (SO2) to CO for about 3000 major cities in the world. This facilitated the identification of contributions from different sources; including vehicles, industries, AWB and forest fires during LD. The comparison between CO levels during the LD and PreLD periods indicates a notable reduction in the global tropospheric CO, but no significant change in the stratosphere. It is found that CO emissions decreased during LD in the hotspot regions, but rebounded after the LD restrictions were lifted. This study, therefore, highlights the importance of policy decisions and their implementations in the global and regional scales to improve the air quality, and thus to protect public health and environment.
The discovery of causal structures behind a phenomenon under investigation has been at the heart of scientific inquiry since the beginning. Randomized control trials, the gold standard for causal analysis, may not always be feasible, such as in the domain of climate sciences. In the absence of interventional data, we are forced to depend only on observational data. This study demonstrates the application of one such causal discovery algorithm using a neural network for identifying the drivers of surface ozone variability in Antarctica. The analyses reveal the overarching influence of the stratosphere on the surface ozone variability in Antarctica, buttressed by the southern annular mode and tropospheric wave forcing in mid-latitudes. We find no significant and robust evidence for the influence of tropical teleconnection on the ground-level ozone in Antarctica. As the field of atmospheric science is now replete with a massive stock of observational data, both satellite and ground-based, this tool for automated causal structure discovery might prove to be invaluable for scientific investigation and flawless decision making.
Atmospheric aerosols play key roles in radiation budget, ecosystem dynamics, air quality and cloud microphysics of a region and thus, they greatly influence the global climate, ecosystem and public health. We present the temporal variability of atmospheric aerosols over India and north Indian Ocean (NIO) for the past two decades (2000–2019). Here, the measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra, Multi-angle Imaging Spectroradiometer (MISR) on Terra, and Advanced Along Track Scanning Radiometer (AATSR) measurements from Envisat, and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data are considered. On average, the Indo Gangetic Plain (IGP) shows the largest (0.5–1.2), and NIO and Himalaya show the smallest (0.1 or smaller) Aerosol Optical depth (AOD) values. The peak AOD loading is observed in June–July-August, and IGP has a secondary peak in March owing to the stubble and biomass burning in winter months. In contrast, the peak aerosol loading in the northeast is in winter due to forest and biomass burning during the period there. The inter-annual variability is very small in NIO, Himalaya and Bay of Bengal. The trends estimated from the combined (AATSR, MISR, MODIS and MERRA-2) data show the highest positive trends at the lower IGP and east central regions, about 0.8–1.2/dec, and are statistically significant. This is consistent with the urban activity, industries and dense population there. However, the Desert, northern Himalaya and northern Arabian Sea show insignificant negative trends, from −0.2 to −0.4/dec, as the anthropogenic sources of aerosols are very limited there. The bias estimate shows that most satellite and reanalysis data are in very good agreement at all regions (within 0.1–0.2). Even though the bias in the measurements are considered, the trends estimated are still large enough to be statistically significant. The analyses, therefore, caution the increasing aerosol loading and their plausible climate feedback in these regions. The assessment also demonstrate the potential of synergetic use of multiple-platform measurements for climate system studies.
India is currently the second-largest emitter of black carbon (BC) in the world, with emissions projected to rise steadily in the coming decades. In view of the large variations associated with BC emission inventories in this region, model outputs of BC mass and radiative forcing (RF) need to be validated against long-term regionally-representative atmospheric measurements. Such measurements are highly scattered spatially as well as temporally in India, and a systematic evaluation of BC data is non-existent so far. To address this issue, we present here a comprehensive review of BC measurements in India from a survey of >140 studies spanning 2002-2018. In addition to summarizing baseline BC levels in urban, semi-urban, rural and remote locations, we report impacts of anomalous environmental and/or emission conditions, e.g., truck/general strikes, firework events, fog/haze episodes, large-scale biomass burning events, etc. We also present a discussion on major BC sources and climate impacts (in terms of direct RF) in major land-use categories, mitigation strategies currently employed on a national scale, and recent advances in measuring brown carbon (BrC) in India. We identify key areas for improvement, such as – i) the need for long-term BC monitoring networks, especially in regions where estimated emissions are high but measurement coverage is low; ii) the general lack of understanding, despite some recent reports, of BC aerosol mixing states, aging and direct climate effects in the Indian context; iii) the need to shift from qualitative approaches of BC source apportionment to robust quantitative measures; and iv) the prospects for coupled chemical-optical characterization of BrC for a better understanding of its sources and climate effects. We list potential research directions for the scientific community to address these knowledge gaps. We also believe that this review will be beneficial to policymakers for prioritizing BC mitigation efforts.
The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA's terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams and converged to a single near-real-time stream in mid-2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).
Black carbon (BC) and PM2.5 were studied for nine years from 2005 to 2013 in the Beijing urban area. The overall weekly average mass concentrations of BC and PM2.5 were 4.3 and 66.8 μg/m³. PM2.5 annual means of the nine years are around 2 times of the standard (GB3095-2012) in China, and are 5-7 times higher than the WHO standard. The Beijing Olympic Games in 2008 was a milestone to mitigate aerosol pollution. Temporal distribution of BC shows a distinct declining trend, and annual mean mass concentrations of PM2.5 after 2008 were lower than those before 2008 but increased from 2011 to 2013. Wind rose plots show that high BC concentrations are usually associated with low wind speed of northeastern or southwestern winds, generally causing poor visibility. Governmental mitigation measures such as traffic restriction despite increased motor vehicle numbers and gasoline consumption and industry relocation with declining consumption of coal and coke were successful in reducing BC emissions. Annual mean of BC was reduced by 38% in 2013 compared to 2005. However, BC contamination in Beijing is still severe when compared to other urban areas around the world.
Black carbon (BC) is a significant component of fine particulate matter (PM2.5) air pollution, which has been linked to a series of adverse health effects, in particular premature mortality. Recent scientific research indicates that BC also plays an important role in climate change. Therefore, controlling black carbon emissions provides an opportunity for a double dividend. This study quantifies the national burden of mortality and morbidity attributable to exposure to ambient BC in the United States (US). We use GEOS-Chem, a global 3-D model of atmospheric composition to estimate the 2010 annual average BC levels at 0.5×0.667° resolution, and then re-grid to 12-km grid resolution across the continental US. Using PM2.5 mortality risk coefficient drawn from the American Cancer Society cohort study, the numbers of deaths due to BC exposure were estimated for each 12-km grid, and then aggregated to the county, state and national level. Given evidence that BC particles may pose a greater risk on human health than other components of PM2.5, we also conducted sensitivity analysis using BC-specific risk coefficients drawn from recent literature. We estimated approximately 14,000 deaths to result from the 2010 BC levels, and hundreds of thousands of illness cases, ranging from hospitalizations and emergency department visits to minor respiratory symptoms. Sensitivity analysis indicates that the total BC-related mortality could be even significantly larger than the above mortality estimate. Our findings indicate that controlling BC emissions would have substantial benefits for public health in the US.
HYSPLIT, developed by NOAA’s Air Resources Laboratory, is one of the most widely used models for atmospheric trajectory and dispersion calculations. We present the model’s historical evolution over the last 30 years from simple hand drawn back trajectories to very sophisticated computations of transport, mixing, chemical transformation, and deposition of pollutants and hazardous materials. We highlight recent applications of the HYSPLIT modeling system, including the simulation of atmospheric tracer release experiments, radionuclides, smoke originated from wild fires, volcanic ash, mercury, and wind-blown dust.
A fuel-based approach is used to assess long-term trends (1970-2010) in mobile source emissions of black carbon (BC) and organic aerosol (OA, including both primary emissions and secondary formation). The main focus of this analysis is the Los Angeles basin, where a long record of measurements is available to infer trends in ambient concentrations of BC and organic carbon (OC), with OC used here as a proxy for OA. Mobile source emissions and ambient concentrations have decreased similarly, reflecting the importance of on- and off-road engines as sources of BC and OA in urban areas. In 1970, the on-road sector accounted for ~90% of total mobile source emissions of BC and OA (primary + secondary). Over time as on-road engine emissions have been controlled, the relative importance of off-road sources has grown. By 2010, off-road engines were estimated to account for 37 ± 20% and 45 ± 16% of total mobile source contributions to BC and OA, respectively, in the Los Angeles area. This study highlights both the success of efforts to control on-road emission sources, and the importance of considering off-road engine and other VOC source contributions when assessing long-term emission and ambient air quality trends.
The global burden of disease study estimated 695,000 premature deaths in 2010 due to continued exposure to outdoor particulate matter and ozone pollution for India. By 2030, the expected growth in many of the sectors (industries, residential, transportation, power generation, and construction) will result in an increase in pollution related health impacts for most cities. The available information on urban air pollution, their sources, and the potential of various interventions to control pollution, should help us propose a cleaner path to 2030. In this paper, we present an overview of the emission sources and control options for better air quality in Indian cities, with a particular focus on interventions like urban public transportation facilities; travel demand management; emission regulations for power plants; clean technology for brick kilns; management of road dust; and waste management to control open waste burning. Also included is a broader discussion on key institutional measures, like public awareness and scientific studies, necessary for building an effective air quality management plan in Indian cities.
Black carbon (BC) plays an important role in both climate change and health impact. Still, BC emissions as well as the historical trends are associated with high uncertainties in existing inventories. In the present study, global BC emissions from 1960 to 2007 were estimated for 64 sources, by using re-compiled fuel consumption and emission factor data set. Annual BC emissions had increased from 5.3 (3.4-8.5 as an interquartile range) to 9.1 (5.6-14.4) teragrams during this period. Our estimations are 11-16% higher than those in previous inventories. Over the period, we found that the BC emission intensity, defined as the amount of BC emitted per unit of energy production, had decreased for all the regions, especially China and India. Improvements in combustion technology and changes in fuel composition had led to an increase in energy use efficiency, and subsequently a decline of BC emission intensities in power plants, the residential sector, and transportation. On the other hand, the BC emission intensities had increased in the industrial and agricultural sectors, mainly due to an expansion of low-efficiency industry (coke and brick production) in developing countries and to an increasing usage of diesel in agriculture in developed countries.
In India, of the 210 GW electricity generation capacity, 66% is derived from coal, with planned additions of 76 GW and 93 GW during the 12th and the 13th five year plans, respectively. Atmospheric emissions from the coal-fired power plants are responsible for a large burden on human health. In 2010–11, 111 plants with an installed capacity of 121 GW, consumed 503 million tons of coal, and generated an estimated 580 ktons of particulates with diameter less than 2.5 μm (PM2.5), 2100 ktons of sulfur dioxides, 2000 ktons of nitrogen oxides, 1100 ktons of carbon monoxide, 100 ktons of volatile organic compounds, and 665 million tons of carbon dioxide. These emissions resulted in an estimated 80,000 to 115,000 premature deaths and 20.0 million asthma cases from exposure to PM2.5 pollution, which cost the public and the government an estimated INR 16,000 to 23,000 crores (USD 3.2 to 4.6 billion). The emissions were estimated for the individual plants and the atmospheric modeling was conducted using CAMx chemical transport model, coupled with plume rise functions and hourly meteorology. The analysis shows that aggressive pollution control regulations such as mandating flue gas desulfurization, introduction and tightening of emission standards for all criteria pollutants, and updating procedures for environment impact assessments, are imperative for regional clean air and to reduce health impacts. For example, a mandate for installation of flue gas desulfurization systems for the operational 111 plants could reduce the PM2.5 concentrations by 30–40% by eliminating the formation of the secondary sulfates and nitrates.
Land surface temperature and emissivity (LST&E) data are essential
for a wide variety of surface-atmosphere studies, from calculating the
evapotranspiration of the Earth's land surface to retrieving atmospheric
water vapor. LST&E products are generated from thermal infrared data
acquired from sensors such as ASTER and MODIS on NASA's EOS platforms.
NASA has identified a major need to develop long-term, consistent
products valid across multiple missions, with well-defined uncertainty
statistics addressing specific Earth science questions. These products
are termed Earth System Data Records (ESDRs) and LST&E have been
identified as an important ESDR. Currently a lack of understanding in
LST&E uncertainties limits their usefulness in land surface and
climate models. To address this issue, a LST&E uncertainty simulator
has been developed to quantify and model uncertainties for a variety of
TIR sensors and LST algorithms. Using the simulator, uncertainties were
estimated for the MODIS and ASTER TES algorithm, including water vapor
scaling (WVS). These uncertainties were parameterized according to view
angle and estimated total column water vapor for application to real
data. The standard ASTER TES algorithm had a RMSE of 3.1 K (1.2 K with
WVS), while the MODIS TES algorithm had a RMSE of 4.5 K (1.5 K with
WVS). Accuracies in retrieved spectral emissivity for both sensors
degraded with higher atmospheric water content, however, with WVS the
emissivity uncertainties were reduced to <0.015. Accurately
quantifying uncertainties in LST&E products not only improves their
utility and understanding but will also enable the data to be fused into
long-term, well characterized ESDRs.
China is one of the countries with the highest ambient particle levels in the world; however, there have been no epidemiologic studies examining the effects of fine particle (PM2.5), coarse particle (PM10-2.5) and black carbon (BC) simultaneously on morbidity outcomes. In this study, we conducted a time-series analysis to evaluate the acute effects of PM2.5, PM10-2.5, and BC on daily hospital visits in Shanghai, China. During our study period, the mean daily concentrations of PM2.5, PM10-2.5 and BC were 53.9μg/m(3), 38.4μg/m(3) and 3.9μg/m(3), respectively. We found significant associations of PM2.5, PM 10-2.5, and BC with daily hospital visits. An inter-quartile range increase of the average concentrations of the current and previous days in PM2.5, PM10-2.5 and BC was associated with a 1.88% (95% CI: 0.69% to 3.06%), a 1.30% (95% CI: 0.25% to 2.34%) and a 1.33% (95% CI: 0.34% to 2.32%) increase in emergency-room visits, respectively. For outpatient visits, the corresponding estimated changes were -2.44% (95% CI: -6.62% to 1.74%), 1.09% (95% CI: -2.72% to 4.90%) and 3.34% (95% CI: 0.10% to 6.57%) respectively. The effects of BC were more robust than the effects of PM2.5 and PM10-2.5 in two-pollutant models. To our knowledge, this is the first study in China, or even in Asian developing countries, to report the effect of PM2.5, PM10-2.5, and BC simultaneously on morbidity. Our findings also suggest that BC could serve as a valuable air quality indicator that reflects the health risks of airborne particles.
Vertical profiles of black carbon (BC) aerosol were determined from aircraft measurements under the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) program conducted by the Indian Institute of Tropical Meteorology, India during 2009 over Bangalore and Hyderabad in south India. BC mass loadings decreased approximately monotonically from 10(3) to 10(4) ng/m(3) at the surface to ~10(2) ng/m(3) at an altitude of about 7 km; although layers at intermediate levels containing anomalously high BC loadings were frequently encountered that were attributed mainly to the convective transport from surface sources accompanied by changes in the local boundary layer and atmospheric stability. In addition, as evidenced from air mass back trajectories; long range transport from distant sources contributed to some anomalous spikes in BC concentration. The presence of BC in cloud forming regions of the free troposphere could have important implications for cloud microphysics and subsequent rainfall mechanism over this region. Apart from this, the effects on human health are equally important.
In this report we review the health effects of three short-lived greenhouse pollutants-black carbon, ozone, and sulphates. We undertook new meta-analyses of existing time-series studies and an analysis of a cohort of 352,000 people in 66 US cities during 18 years of follow-up. This cohort study provides estimates of mortality effects from long-term exposure to elemental carbon, an indicator of black carbon mass, and evidence that ozone exerts an independent risk of mortality. Associations among these pollutants make drawing conclusions about their individual health effects difficult at present, but sulphate seems to have the most robust effects in multiple-pollutant models. Generally, the toxicology of the pure compounds and their epidemiology diverge because atmospheric black carbon, ozone, and sulphate are associated and could interact with related toxic species. Although sulphate is a cooling agent, black carbon and ozone could together exert nearly half as much global warming as carbon dioxide. The complexity of these health and climate effects needs to be recognised in mitigation policies.
Long-term exposure to traffic-related air pollution and the risk of coronary heart disease hospitalization and mortality
- Gan
Health effects of black carbon. World Health Organization. Regional Office for Europe
- N A Janssen
- M E Gerlofs-Nijland
- T Lanki
- R O Salonen
- F Cassee
- G Hoek
- P Fischer
- B Brunekreef
- M Krzyzanowski