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Finding environmentally significant clusters in global supply-chain networks of goods and services has been investigated by Kagawa et al. (Soc Netw 35(3):423-438, 2013a; Econ Syst Res 25(3):265-286, 2013b; Glob Environ Chang, 2015), using the popular clustering method of nonnegative matrix factorization, which actually yields sensitive cluster assignments. Due to this sensitivity issue, there is a danger of overfitting of the results. In order to confirm the robustness of the obtained clusters, which in fact have strong implications for international climate change mitigation, especially for the US-induced Chinese clusters, we design a simulation-based experiment. Empirical findings of the proposed approach are compared with those of Kagawa et al. (Glob Environ Chang, 2015). The environmental implications are reported as well. [ABSTRACT FROM AUTHOR]

Abstract: The aggressive traffic interventions and emission control measures implemented to improve air quality during the 2008 Beijing Olympic Games created a valuable case study to evaluate the effectiveness of these measures on mitigating air pollution and protecting public health. In this paper, we report the results from our field campaign in summer 2008 on the on-road emission factors of carbon monoxide, black carbon (BC) and ultrafine particles (UFP) as well as the ambient BC concentrations. The fleet average emission factors for light-duty gasoline vehicles (LDGV) showed considerable reduction in the Olympic year (2008) compared to the pre-Olympic year (2007). Our measurement of Black Carbon (BC), a primary pollutant, at different elevations at the ambient site suggests consistent decrease in BC concentrations as the height increased near the ground level, which indicates that the nearby ground level sources, probably dominated by traffic, contributed to a large portion of BC concentrations in the lower atmospheric layer in Beijing during summertime. These observations indicate that people living in near ground levels experience higher exposures than those living in higher floors in Beijing. The BC diurnal patterns on days when traffic control were in place during the Olympic Games were compared to those on non-traffic-control days in both 2007 and in 2008. These patterns strongly suggest that diesel trucks are a major source of summertime BC in Beijing. The median BC concentration on Olympic days was 3.7 μg m−3, which was dramatically lower than the value on non-traffic-control days, indicating the effectiveness of traffic control regulations in BC reduction in Beijing. [Copyright &y& Elsevier]

We have used a global version of the Regional Air Pollution Information and Simulation (RAINS) model to estimate anthropogenic emissions of the air pollution precursors sulphur dioxide (SO2), nitrogen oxides (NO x ), carbon monoxide (CO), primary carbonaceous particles of black carbon (BC), organic carbon (OC) and methane (CH4). We developed two scenarios to constrain the possible range of future emissions. As a baseline, we investigated the future emission levels that would result from the implementation of the already adopted emission control legislation in each country, based on the current national expectations of economic development. Alternatively, we explored the lowest emission levels that could be achieved with the most advanced emission control technologies that are on the market today. This paper describes data sources and our assumptions on activity data, emission factors and the penetration of pollution control measures. We estimate that, with current expectations on future economic development and with the present air quality legislation, global anthropogenic emissions of SO2 and NO x would slightly decrease between 2000 and 2030. For carbonaceous particles and CO, reductions between 20% and 35% are computed, while for CH4 an increase of about 50% is calculated. Full application of currently available emission control technologies, however, could achieve substantially lower emissions levels, with decreases up to 30% for CH4, 40% for CO and BC, and nearly 80% for SO2. [Copyright &y& Elsevier]

Background: Due to the consequence of industrial CO emission, the surface temperature of Earth is rising at an alarming rate, causing accelerating deadly effects on the environment and living beings. Thus, an approach to mitigate global warming has been proposed by breaking down industrial CO and converting it into useful products. Methods: Lithium nitride (LiN), the only stable alkali metal nitride that reacts readily with CO, has been proposed to use as a means to break down CO. For this, a chemical reactor has been proposed to install at industrial CO-releasing tower to capture CO and allow a heat-releasing reaction into the bioreactor in the presence of LiN to convert it into carbon (C), nitrogen (N), oxygen (O), and lithium cyanamide (LiCN). In order to confirm this theoretical viability of this technology, a series of global environmental issues has also been discussed to neutralize global warming by utilizing this simple technology. Results: The by-products lithium cyanamide (LiCN) is environmentally benign and can be used as fertilizer, and subsequently, the additional products (C, N, and O) from this reaction naturally will mitigate the atmospheric and biospheric chemical cycle. Conclusions: The technology indeed would be an innovative field of carbon management science which will not only break down the deadly CO but also produce useful products like LiCN (a fertilizer) and C, N, and O to balance the atmosphere and biosphere; thus, global warming comes to an end in the near future. [ABSTRACT FROM AUTHOR]

In this study, we present long-term near-surface measurements of sulfur dioxide (SO), oxides of nitrogen (NO), carbon monoxide (CO), and ozone (O) carried out at an urban location, Kanpur (26.46°N, 80.33°E, 125 m amsl), in Northern India from June 2009 to May 2013. The mean concentrations of SO, NO, CO, and O over the entire study period were 3.0, 5.7, 721, and 27.9 ppb, respectively. SO, NO and CO concentrations were highest during the winter season, whereas O concentration peaked during summer. The former could be attributed mainly to the near-surface anthropogenic sources (e.g. automobiles, residential cooking, brick kilns, coal-burning power plants, agricultural land-clearing, and biomass burning) and low mixing height in winter, whereas the latter was clearly due to enhanced chemical production of O during the pre-monsoon (i.e. summer) season. The lowest concentration of all trace gases were observed during the monsoon season, due to efficient wet scavenging by precipitation. The averaged diurnal patterns also showed similar seasonal variation. NO and CO showed peaks during morning and evening traffic hours and a valley in the afternoon irrespective of the seasons, clearly linked to the boundary layer height evolution. Contrarily, O depicted a reverse pattern with highest concentrations during afternoon hours and lowest in the morning hours. The mean rate of change of O concentrations (dO/dt) during the morning hours (08:00 to 11:00 h) and evening hours (17:00 to 19:00 h) at Kanpur were 3.3 ppb h and −2.6 ppb h, respectively. O followed a positive linear relationship with temperature, except in post-monsoon season while the strong negative with the relative humidity in all seasons. The ventilation coefficient was found to be highest in the pre-monsoon season (15,622 m s) and lowest during winter (2564 m s), indicative of excellent pollution dispersion efficiency during the pre-monsoon season. However, the low ventilation coefficient during winter and post-monsoon seasons indicated that the high-pollution potential occurs at this site. [ABSTRACT FROM AUTHOR]

In this study, global emissions of gases and particles from the transportation sector are projected from the year 2010 to 2050. The Speciated Pollutant Emission Wizard (SPEW)-Trend model, a dynamic model that links the emitter population to its emission characteristics, is used to project emissions from on-road vehicles and non-road engines. Unlike previous models of global emission estimates, SPEW-Trend incorporates considerable detail on the technology stock and builds explicit relationships between socioeconomic drivers and technological changes, such that the vehicle fleet and the vehicle technology shares change dynamically in response to economic development. Emissions from shipping, aviation, and rail are estimated based on other studies so that the final results encompass the entire transportation sector. The emission projections are driven by four commonly-used IPCC (Intergovernmental Panel on Climate Change) scenarios (A1B, A2, B1, and B2). With global fossil-fuel use (oil and coal) in the transportation sector in the range of 128-171 EJ across the four scenarios, global emissions are projected to be 101-138 Tg of carbon monoxide (CO), 44-54 Tg of nitrogen oxides (NOx), 14-18 Tg of nonmethane total hydrocarbons (THC), and 3.6-4.4 Tg of particulate matter (PM) in the year 2030. At the global level, a common feature of the emission scenarios is a projected decline in emissions during the first one or two decades (2010-2030), because the effects of stringent emission standards offset the growth in fuel use. Emissions increase slightly in some scenarios after 2030, because of the fast growth of onroad vehicles with lax or no emission standards in Africa and increasing emissions from non-road gasoline engines and shipping. On-road vehicles and non-road engines contribute the most to global CO and THC emissions, while on-road vehicles and shipping contribute the most to NOx and PM emissions. At the regional level, Latin America and East Asia are the two largest contributors to global CO and THC emissions in the year 2010; this dominance shifts to Africa and South Asia in the future. By the year 2050, for CO and THC emissions, non-road engines contribute the greatest fraction in Asia and the former USSR, while on-road vehicles make the largest contribution in Latin America, Africa, and the Middle East; for NOx and PM emissions, shipping controls the trend in most regions. These forecasts include a formal treatment of the factors that drive technology choices in the global vehicle sector and therefore represent a robust and plausible projection of what future emissions may be. These results have important implications for emissions of gases and aerosols that influence air quality, human health, and climate change. [ABSTRACT FROM AUTHOR]

It has become possible to retrieve the global, longterm trends of trace gases that are important to atmospheric chemistry, climate, and air quality from satellite data records that span more than a decade. However, many of the satellite remote sensing techniques produce measurements that have variable sensitivity to the vertical profiles of atmospheric gases. In the case of constrained retrievals like optimal estimation, this leads to a varying amount of a priori information in the retrieval and is represented by an averaging kernel (AK). In this study, we investigate to what extent the estimation of trends from retrieved data can be biased by temporal changes of averaging kernels used in the retrieval algorithm. In particular, the surface carbon monoxide data retrieved from the Measurements Of Pollution In The Troposphere (MOPITT) instrument from 2001 to 2010 were analyzed. As a practical example based on the MOPITT data, we show that if the true atmospheric mixing ratio is continuously 50% higher or lower than the a priori state, the temporal change of the averaging kernel at the surface level gives rise to an artificial trend in retrieved surface carbon monoxide, ranging from -10.71 to +13.21 ppbv yr-1 (-5.68 to +8.84%yr-1) depending on location. Therefore, in the case of surface (or near-surface level) CO derived from MOPITT, the AKs trends multiplied by the difference between true and a priori states must be quantified in order to estimate trend biases. [ABSTRACT FROM AUTHOR]

Spatially precise forecasts of the impacts of climate change on the distribution of major vegetation types are essential for the implementation of effective conservation and land use policy. However, existing studies frequently omit major sources of climate variability that can significantly increase the uncertainty of projections. In this study we demonstrate how different predictions for sea surface temperature (SST) for the first half of the twenty-first century increase the uncertainty associated with forecasts of the future distribution of major ecosystems in South America. This is demonstrated through a numerical experiment using a coupled climate-vegetation model (CCM3-IBIS) for IPCC emission scenario A2 that incorporates the SST data from ten different models. The study reveals an increasing uncertainty in the ability to forecast future vegetation patterns, such that by 2050 the simulation is unable to robustly forecast the vegetation cover in an area equivalent to 28 % in South America (5 × 10 km). The future of the central and northeastern regions of Brazil is especially uncertain, with outcomes, ranging from savanna, and open shrubland to grassland. Recognizing and managing such uncertainty should be a priority for decision makers. [ABSTRACT FROM AUTHOR]

The interrelated geographic and temporal variability seen in more than seven years of tropical and subtropical upper tropospheric (215 hPa) ozone, carbon monoxide and cloud ice water content (IWC) observations by the Aura Microwave Limb Sounder (MLS) are presented. Observed ozone abundances and their variability (geographic and temporal) agree to within 10-15 ppbv with records from sonde observations. MLS complements these (and other) observations with global coverage and simultaneous measurements of related parameters. Previously-reported phenomena such as the ozone "wave one" feature are clearly seen in the MLS observations, as is a double peak in ozone abundance over tropical East Africa, with enhanced abundances in both May to June and September to November. While repeatable seasonal cycles are seen in many regions, they are often accompanied by significant interannual variability. Ozone seasonal cycles in the southern tropics and subtropics tend to be more distinct (i.e., annually repeatable) than in the northern. By contrast, carbon monoxide shows distinct seasonal cycles in many northern subtropical regions, notably from India to the Eastern Pacific. Deep convection (as indicated by large values of IWC) is typically associated with reductions in upper tropospheric ozone. Convection over polluted regions is seen to significantly enhance upper tropospheric carbon monoxide. While some regions show statistically significant correlations among ozone, carbonmonoxide and IWC, simple correlations fall well short of accounting for the observed variability. The observed interrelated variations and metrics of nnual and interannual variability described here represent a new resource for validation of atmospheric chemistry models. [ABSTRACT FROM AUTHOR]

A four-dimensional variational data assimilation system (4D-Var) is developed to retrieve carbon monoxide (CO) fluxes at regional scale, using an air quality network. The air quality stations that monitor CO are proximity stations located close to industrial, urban or traffic sources. The mismatch between the coarsely discretised Eulerian transport model and the observations, inferred to be mainly due to representativeness errors in this context, lead to a bias (average simulated concentrations minus observed concentrations) of the same order of magnitude as the concentrations. 4D-Var leads to a mild improvement in the bias because it does not adequately handle the representativeness issue. For this reason, a simple statistical subgrid model is introduced and is coupled to 4D-Var. In addition to CO fluxes, the optimisation seeks to jointly retrieve influence coefficients, which quantify each station's representativeness. The method leads to a much better representation of the CO concentration variability, with a significant improvement of statistical indicators. The resulting increase in the total inventory estimate is close to the one obtained from remote sensing data assimilation. This methodology and experiments suggest that information useful at coarse scales can be better extracted from atmospheric constituent observations strongly impacted by representativeness errors. [ABSTRACT FROM AUTHOR]

ABSTRACT Regional aerosol concentrations are governed by an evolving balance between aerosol sources and sinks. Here, a simple technique is described for making estimates of the extent to which seasonal aerosol variability is controlled by wet scavenging rather than the efficiency of transport from pollution source regions. Carbon monoxide (CO) is employed as an assumed passive tracer of pollution transport efficiency, to which the magnitude of aerosol light scattering is compared. Because aerosols, unlike CO, are affected by wet scavenging as well as transport efficiency, the ratio of short-term perturbations in these two quantities provides a measure of the relative roles of these two processes. This technique is applied to surface measurements in the Arctic at Barrow, Alaska (71°N) for the decade between 2000 and 2009. What is found is that a well-known seasonal cycle in 'Arctic Haze' is dominated by variability in wet scavenging. Crossing the freezing threshold for warm rain production appears particularly critical for efficiently cleaning the air. [ABSTRACT FROM AUTHOR]