Characteristics influencing the variability of urban CO2 fluxes in Melbourne,Australia

Urban areas are significant contributors to global carbon dioxide emissions. Vehicle emissions and other anthropogenic related activities are a frequent source of CO₂ to the atmosphere, contributing to global warming. Micrometeorological techniques used for observations in Northern Hemisphere cities have found that urban CO₂ fluxes are consistently a source. This study investigates CO₂ fluxes in an Australian city, adding to the global database of CO₂ fluxes in a bid to aid in future development of planning policies concerning reductions in CO₂ emissions. Using the eddy covariance approach, fluxes of CO₂ were measured at a suburban site (Preston) in Melbourne, Australia from February 2004 to June 2005 to investigate temporal variability. A second site (Surrey Hills) with differing surface characteristics (in particular, greater vegetation cover) was also established in Melbourne and ran simultaneously for 6 months (February 2004–July 2004). Results showed that both sites were a net source of CO₂ to the atmosphere. Diurnal patterns of fluxes were largely influenced by traffic volumes, with two distinct peaks occurring at the morning and evening traffic peak hours, with the winter morning peak averaging 10.9 μmol m⁻² s⁻¹ at Preston. Summer time fluxes were lower than during winter due to greater vegetative influence and reduced natural gas combustion. Vegetation limited the source of CO₂ in the afternoon, yet was not enough to combat the strong local anthropogenic emissions. Surrey Hills showed higher fluxes of CO₂ despite greater vegetation cover because of higher local traffic volumes. Annual emissions from Preston were estimated at 84.9 t CO₂ ha⁻¹ yr⁻¹. Magnitudes and patterns of suburban CO₂ fluxes in Melbourne were similar to those observed in Northern Hemisphere suburban areas.     

Numerical model for static chamber measurement of multi-component landfill gas emissions and its application

The quantitative assessment of landfill gas emissions is essential to assess the performance of the landfill cover and gas collection system. The relative error of the measured surface emission of landfill gas may be induced by the static flux chamber technique. This study aims to quantify effects of the size of the chamber, the insertion depth, pressure differential on the relative errors by using an integrated approach of in situ tests, and numerical modeling. A field experiment study of landfill gas emission is conducted by using a static chamber at one landfill site in Xi’an, Northwest China. Additionally, a two-dimensional axisymmetric numerical model for multi-component gas transport in the soil and the static chamber is developed based on the dusty-gas model (DGM). The proposed model is validated by the field data obtained in this study and a set of experimental data in the literature. The results show that DGM model has a better capacity to predict gas transport under a wider range of permeability compared to Blanc’s method. This is due to the fact that DGM model can explain the interaction among gases (e.g., CH₄, CO₂, O₂, and N₂) and the Knudsen diffusion process while these mechanisms are not included in Blanc’s model. Increasing the size and the insertion depth of static chambers can reduce the relative error for the flux of CH₄ and CO₂. For example, increasing the height of chambers from 0.55 to 1.1 m can decrease relative errors of CH₄ and CO₂ flux by 17% and 18%, respectively. Moreover, we find that gas emission fluxes for the case with positive pressure differential (?P_in-out) are greater than that of the case without considering pressure fluctuations. The Monte Carlo method was adopted to carry out the statistical analysis for quantifying the range of relative errors. The agreement of the measured field data and predicted results demonstrated that the proposed model has the capacity to quantify the emission of landfill gas from the landfill cover systems.

     

Surface Flux Measurements of CO2 and N2O from a Dried Rice Paddy in Japan during a Fallow Winter Season

Abstract

The CO₂ and N₂O soil emissions at a rice paddy in Mase, Japan, were measured by enclosures during a fallow winter season. The Mase site, one of the AsiaFlux Network sites in Japan, has been monitored for moisture, heat, and CO₂ fluxes since August 1999. The paddy soil was found to be a source of both CO₂ and N₂O flux from this experiment. The CO₂ and N₂O fluxes ranged from -27.6 to 160.4μg CO₂/m²/sec (average of 49.1 ± 42.7 μg CO₂/m²/sec) and from -4.4 to 129.5 ng N₂O/m²/sec (average of 40.3 ± 35.6 ng N₂O/m²/sec), respectively. A bimodal trend, which has a sub-peak in the morning around 10:00 a.m. and a primary peak between 2:00 and 3:00 p.m., was observed. Gas fluxes increased with soil temperature, but this temperature dependency seemed to occur only on the calm days. Average CO₂ and N₂O fluxes were 27.7 μg CO₂/m²/sec and 13.4 ng N₂O/m²/sec, with relatively small fluctuation during windy days, while averages of 69.3 μg CO₂/m²/sec and 65.8 ng N₂O/m²/sec were measured during calm days. This relationship was thought to be a result of strong surface winds, which enhance gas exchange between the soil surface and the atmosphere, thus reducing the gas emissions from soil surfaces.     

Toward verifying fossil fuel CO2 emissions with the CMAQ model: Motivation,model description and initial simulation

Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO₂ spatiotemporal variability and verify CO₂ fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO₂. This paper presents methods, input data, and initial results for CO₂ simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere–atmosphere exchange, and meteorology in regulating the spatial distribution of CO₂ near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO₂ concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver, CO, the CMAQ model using hourly varying, high-resolution CO₂ fossil-fuel emissions from the Vulcan inventory and CarbonTracker optimized NEE reproduced the observed diurnal profile of CO₂ reasonably well but with a low bias in the early morning. The spatial distribution of CO₂ was found to correlate with NOx, SO₂, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO₂ observations and understand CO₂ variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO₂ source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO₂ spatiotemporal variability and various uncertainties in the future.

Implications: Atmospheric CO₂ has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO₂ variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO₂ emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO₂ as an air pollutant.     

Comparison of manual and automated chambers for field measurements of N2O,CH4, CO2 fluxes from cultivated land

Chamber techniques can easily be applied to field trials with multiple small plots measuring carbon- and nitrogen-trace gas fluxes. Nevertheless, such chamber measurements are usually made weekly and rarely more frequently than once daily. However, automatic chambers do allow flux measurements on sub-daily time scales. It has been hypothesized that sub-daily measurements provide more reliable results, as diurnal variations are captured better compared to manual measurements. To test this hypothesis we compared automatic and manual measurements of N₂O, CO₂ and CH₄ fluxes from tilled and non-tilled plots of a rice–wheat rotation ecosystem over a non-waterlogged period. Our results suggest that both techniques, i.e., either manual or automatic chambers of N₂O and CO₂ emissions resulted in biased fluxes. The manual measurements were adequate to capture either day-to-day or seasonal dynamics of N₂O, CO₂ and CH₄ exchanges, but overestimated the cumulative N₂O and CO₂ emissions by 18% and 31%, respectively. This was due to neglecting temperature-dependent diurnal variations of C and N trace gas fluxes. However, the automatic measurements underestimated the cumulative emissions of N₂O and CO₂ by 22% and 17%, respectively. This underestimation resulted from chamber effects upon soil moisture during rainfall processes. No significant difference was detected between the two methods in CH₄ exchanges over the non-waterlogged soils. The bias of manual chambers may be significant when pronounced diurnal variations occur. The bias of automatic measurements can only be avoided/minimized if chamber positions are frequently changed and/or if chambers are automatically opened during rainfall events. We therefore recommend using automatic chambers together with continuous measurements of soil chamber moisture to allow for soil moisture correction of fluxes or to correct flux estimates as derived by manual chambers for possible diurnal variations.     

Estimates of carbon dioxide emissions based on incomplete condition information: a case study of liquefied natural gas in China

Recent calculations of carbon dioxide (CO₂) emissions have faced challenges because data consist of only partial information, which is called “incomplete information.” According to the emission factor method, energy consumption and CO₂ emission factors with incomplete information may lead to unmatched multiplication between themselves, which affects accuracy and increases uncertainties in emission results. To address a specific case of incomplete information that has not been fully explored, we studied the effects of incomplete condition information on the estimates of CO₂ emissions from liquefied natural gas (LNG) in China. Based on Chinese LNG sampling data, we obtained the specific-country CO₂ emission factor for LNG in China and calculated the corresponding CO₂ emissions. By applying hypothesis testing, regression analysis, variance analysis, or Monte Carlo (MC) simulations, the effects of incomplete information on the uncertainty of CO₂ emission calculations in three cases were analyzed. The results indicate that calorific values have more than a 9.8% impact on CO₂ emission factors and CO₂ emissions with incomplete sample information. Regarding incomplete statistical information, the impact of statistical temperature on CO₂ emissions exceeds 5.5%. Regarding incomplete sample and statistical information, sample and statistical temperatures can individually increase estimate biases by more than 5.2%. Significantly, the impacts of sample temperature and statistical temperature may offset each other. Therefore, the incomplete condition information is quite important and cannot be ignored in the estimation of CO₂ emissions from LNG and international fair comparison.

     

Soil CO2 fluxes from direct seeding rice fields under two tillage practices in central China

Agricultural practices affect the production and emission of carbon dioxide (CO₂) from paddy soils. It is crucial to understand the effects of tillage and N fertilization on soil CO₂ flux and its influencing factors for a better comprehension of carbon dynamics in subtropical paddy ecosystems. A 2-yr field study was conducted to assess the effects of tillage (conventional tillage [CT] and no-tillage [NT]) and N fertilization (0 and 210 kg N ha^?1) on soil CO₂ fluxes during the 2008 and 2009 rice growing seasons in central China. Treatments were established following a split-plot design of a randomized complete block with tillage practices as the main plot and N fertilizer level as the split-plot treatment. The soil CO₂ fluxes were measured 24 times in 2008 and 17 times in 2009. N fertilization did not affect soil CO₂ emissions while tillage affected soil CO₂ emissions, where NT had similar soil CO₂ emissions to CT in 2008, but in 2009, NT significantly increased soil CO₂ emissions. Cumulative CO₂ emissions were 2079–2245 kg CO₂–C ha^?1 from NT treatments, and 2084–2141 kg CO₂–C ha^?1 from CT treatments in 2008, and were 1257–1401 kg CO₂–C ha^?1 from NT treatments, and 1003–1034 kg CO₂–C ha^?1 from CT treatments in 2009, respectively. Cumulative CO₂ emissions were significantly related to aboveground biomass and soil organic C. Before drainage of paddy fields, soil CO₂ fluxes were significantly related to soil temperature with correlation coefficients (R) of 0.67–0.87 in 2008 and 0.69–0.85 in 2009; moreover, the Q₁₀ values ranged from 1.28 to 1.55 and from 2.10 to 5.21 in 2009, respectively. Our results suggested that NT rice production system appeared to be ineffective in decreasing carbon emission, which suggested that CO₂ emissions from integrated rice-based system should be taken into account to assess effects of tillage.     

Methane emissions from peat bogs in the vicinity of the Mace Head Atmospheric Research Station over a 12-year period

Methane emissions from the peat bogs in Connemara, Ireland have been inferred from the trace gas observations at the Mace Head Atmospheric Research Station using the nocturnal box method. A total of 237 local events, during April to September, over a 12-year period have been studied. Simultaneous emissions of methane, carbon dioxide and chloroform are routinely observed under nocturnal inversions with low wind speeds from the peat bogs proximal to Mace Head. Night-time deposition of ozone and hydrogen occurs concurrently with these emissions. Using the temporally correlated methane and ozone data we estimate methane emissions from each event. Simultaneous methane and chloroform emissions, together with ozone and hydrogen deposition have been characterised, leading to the estimation of methane emission rates for each event. The mean methane emission flux was found to be 400 ± 90 ng m^?2 s^?1. A strong seasonal cycle was found in the methane emission fluxes but there was little evidence of a long-term trend in the emissions from the peat bogs in the vicinity of the Mace Head station.     

Transpiration of gaseous elemental mercury through vegetation in a subtropical wetland in Florida

Four seasonal sampling campaigns were carried out in the Florida Everglades to measure elemental Hg vapor (Hg°) fluxes over emergent macrophytes using a modified Bowen ratio gradient approach. The predominant flux of Hg° over both invasive cattail and native sawgrass stands was emission; mean day time fluxes over cattail ranged from ∼20 (winter) to ∼40 (summer) ng m⁻² h⁻¹. Sawgrass fluxes were about half those over cattail during comparable periods. Emission from vegetation significantly exceeded evasion of Hg° from the underlying water surface (∼1–2 ng m⁻² h⁻¹) measured simultaneously using floating chambers. Among several environmental factors (e.g. CO₂ flux, water vapor flux, wind speed, water, air and leaf temperature, and solar radiation), water vapor exhibited the strongest correlation with Hg° flux, and transpiration is suggested as an appropriate term to describe this phenomenon. The lack of significant Hg° emissions from a live, but uprooted (floating) cattail stand suggests that a likely source of the transpired Hg° is the underlying sediments. The pattern of Hg° fluxes typically measured indicated a diel cycle with two peaks, possibly related to different gas exchange dynamics: one in early morning related to lacunal gas release, and a second at midday related to transpiration; nighttime fluxes approached zero.     

Carbon dioxide fluxes over an urban park area

From September 2006 to October 2007 turbulent fluxes of carbon dioxide were measured at an urban tower station (26 m above ground level, z/z_h = 1.73) in Essen, Germany, using the eddy covariance technique. The site was located at the border between a public park area (70 ha) in the south–west of the station and suburban/urban residential as well as light commercial areas in the north and east of the tower. Depending on the land-use two different sectors (park and urban) were identified showing distinct differences in the temporal evolution of the surface-atmosphere exchange of CO₂. While urban fluxes appear to be governed by anthropogenic emissions from domestic heating and traffic (average flux 9.3 μmol m^?2 s^?1), the exchange of CO₂ was steered by biological processes when the park contributed to the flux footprint. The diurnal course during the vegetation period exhibited negative daytime fluxes up to ?10 μmol m^?2 s^?1 on average in summer. Nevertheless, with a mean of 0.8 μmol m^?2 s^?1 park sector fluxes were slightly positive, thus no net carbon uptake by the surface occurred throughout the year.In order to sum the transport of CO₂ a gap-filling procedure was performed by means of artificial neural network generalisation. Using additional meteorological inputs the daily exchange of CO₂ was reproduced using radial basis function networks (RBF). The resulting yearly sum of 6031 g m^?2 a^?1 indicates the entire study site to be a considerable source of CO₂.     

Direct Measurement of Fugitive Emissions of Hydrocarbons from a Refinery

Abstract

Refineries are a source of emissions of volatile hydrocarbons that contribute to the formation of smog and ozone. Fugitive emissions of hydrocarbons are difficult to measure and quantify. Currently these emissions are estimated based on standard emission factors for the type and use of equipment installed. Differential absorption light detection and ranging (DIAL) can remotely measure concentration profiles of hydrocarbons in the atmosphere up to several hundred meters from the instrument. When combined with wind speed and direction, downwind vertical DIAL scans can be used to calculate mass fluxes of the measured gas leaving the site. Using a mobile DIAL unit, a survey was completed at a Canadian refinery to quantify fugitive emissions of methane, C₂₊ hydrocarbons, and benzene and to apportion the hydrocarbon emissions to the various areas of the refinery. Refinery fugitive emissions as measured with DIAL during this demonstration study were 1240 kg/hr of C₂₊ hydrocarbons, 300 kg/hr of methane, and 5 kg/hr of benzene. Storage tanks accounted for over 50% of the total emissions of C₂₊ hydrocarbons and benzene. The coker area and cooling towers were also significant sources. The C₂₊ hydrocarbons emissions measured during the demonstration amounted to 0.17% of the mass of the refinery hydrocarbon throughput for that period. If the same loss were repeated throughout the year, the lost product would represent a value of US$3.1 million/yr (assuming US$40/bbl). The DIAL-measured hourly emissions of C₂₊ hydrocarbons were 15 times higher than the emission factor estimates and gave a different perspective on which areas of the refinery were the main source of emissions. Methods, such as DIAL, that can directly measure fugitive emissions would improve the effectiveness of efforts to reduce emissions, quantify the reduction in emissions, and improve the accuracy of emissions data that are reported to regulators and the public.     

Responses of CH(4), CO(2) and N(2)O fluxes to increasing nitrogen deposition in alpine grassland of the Tianshan Mountains

To assess the effects of nitrogen (N) deposition on greenhouse gas (GHG) fluxes in alpine grassland of the Tianshan Mountains in central Asia, CH₄, CO₂ and N₂O fluxes were measured from June 2010 to May 2011. Nitrogen deposition tended to significantly increase CH₄ uptake, CO₂ and N₂O emissions at sites receiving N addition compared with those at site without N addition during the growing season, but no significant differences were found for all sites outside the growing season. Air temperature, soil temperature and water content were the important factors that influence CO₂ and N₂O emissions at year-round scale, indicating that increased temperature and precipitation in the future will exert greater impacts on CO₂ and N₂O emissions in the alpine grassland. In addition, plant coverage in July was also positively correlated with CO₂ and N₂O emissions under elevated N deposition rates. The present study will deepen our understanding of N deposition impacts on GHG balance in the alpine grassland ecosystem, and help us assess the global N effects, parameterize Earth System models and inform decision makers.     

Spatiotemporal analysis of traffic emissions in over 5000 municipal districts in Brazil

Exposure to traffic emission is harmful to human health. Emission inventories are essential to public health policies aiming at protecting human health, especially in areas with incomplete or nonexistent air pollution monitoring networks. In Brazil, for example, only 1.7% of municipal districts have a monitoring network, and only a few studies have reported data on vehicle emission inventories. No studies have presented emission inventories by municipality. In this study, we predicted vehicular emissions for 5570 municipal districts in Brazil during the period 2001–2012. We used a top-down method to estimate emissions. Carbon dioxide (CO₂) is the pollutant with the highest emissions, with approximately 190 million tons per year during the period 2001–2012). For the other traffic-related pollutants, we predicted annual emissions of 1.5 million tons for carbon monoxide (CO), 1.2 million tons of nitrogen oxides (NO_x), 209,000 tons of nonmethane hydrocarbons (NMHC), 58,000 tons of particulate matter (PM), and 42,000 tons for methane (CH₄). From 2001 to 2012, CO, NMHC, and PM emissions decreased by 41, 33, and 47%, respectively, whereas those CH₄, NO_x, and CO₂ increased by 2, 4, and 84%, respectively. We estimated uncertainties in our study and found that NO_x was the pollutant with the lowest percentage difference, 8%, and NMHC with the highest one, 30%. For CO, CH₄, CO₂, and PM, the values were 22, 14, 21, and 20%, respectively. Finally, we found that during 2001 and 2012 emissions increased in the Northwest and Northeast. In contrast, pollutant emissions, except for CO₂, decreased in the Southeast, South, and part of Midwest. Our predictions can be critical to efforts developing cost-effective public policies tailored to individual municipal districts in Brazil.

Implications: Emission inventories may be an alternative approach to provide data for air quality forecasting in areas where air quality data are not available. This approach can be an effective tool in developing spatially resolved emission inventories.     

An urban boreal lake basin as a source of CO₂ and CH₄

Up to now, carbon gas fluxes from urban lakes in the boreal zone have seldom been studied. In summer 2005 we investigated fluxes from an urban boreal lake basin in southern Finland with long history of eutrophication and anoxia. Hypolimnetic CO₂ and CH₄ concentrations were high compared to other boreal lakes. During the open-water period, the lake basin acted as a source of CO₂ and CH₄ with fluxes of 2.10 mol m⁻² and 0.04 mol m⁻², respectively. Despite the high oxidation rate (83%), CH₄ flux was higher than in other lakes and CH₄ contributed 60% to Global Warming Potential. The ratio of carbon emission to accumulation was 4, i.e. emissions were an important route for carbon departure but less so than in rural lakes. Since the lake oxygen conditions affected nutrient availability, there was a positive feedback from hypolimnion to carbon uptake, which was reflected in gas concentrations.     

A model for NOx–O3–terpene chemistry in chamber measurements of plant gas exchange

Cuvette measurements are a tool to analyse CO₂ exchange, transipiration and deposition/emission of different trace gases by plants. To verify these experimental methods and to use them efficiently we have developed a numerical model with atmospheric chemical reactions. The model includes reactions between 54 different chemical species in the gas phase. Using the model we are able to determine optimal size/flow rate ratios and cuvette cycles (closure times) from an experimental point of view. Using the cuvette model with atmospheric chemistry more accurate estimates for emissions/deposition rates of different species can be found. Some chemical reactions are significant, e.g. for NO and terpenes, as regards the analysis and interpretation of measured concentrations. With slower flow rates through a cuvette the significance of reactions is more pronounced. However, there are some species like ozone, where stomatal deposition is a dominant phenomenon and chemistry plays a minor role.     

Carbon emission from global hydroelectric reservoirs revisited

Substantial greenhouse gas (GHG) emissions from hydropower reservoirs have been of great concerns recently, yet the significant carbon emitters of drawdown area and reservoir downstream (including spillways and turbines as well as river reaches below dams) have not been included in global carbon budget. Here, we revisit GHG emission from hydropower reservoirs by considering reservoir surface area, drawdown zone and reservoir downstream. Our estimates demonstrate around 301.3 Tg carbon dioxide (CO₂)/year and 18.7 Tg methane (CH₄)/year from global hydroelectric reservoirs, which are much higher than recent observations. The sum of drawdown and downstream emission, which is generally overlooked, represents 42 % CO₂ and 67 % CH₄ of the total emissions from hydropower reservoirs. Accordingly, the global average emissions from hydropower are estimated to be 92 g CO₂/kWh and 5.7 g CH₄/kWh. Nonetheless, global hydroelectricity could currently reduce approximate 2,351 Tg CO₂eq/year with respect to fuel fossil plant alternative. The new findings show a substantial revision of carbon emission from the global hydropower reservoirs.     

Influencing factors of carbon emissions and their trends in China and India: a machine learning method

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO₂) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO₂ emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO₂ emissions; correspondingly, this study builds a model for CO₂ emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO₂ emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO₂ emissions. Finally, this study forecasted a CO₂ emission trend, with a slowdown predicted in China by 2022; however, CO₂ emission’s reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO₂ emissions without harming economic development.

     

Isoprene emissions from a Florida scrub oak species grown in ambient and elevated carbon dioxide

The emission of isoprene (2-methyl-1,3-butadiene) by terrestrial vegetation is an important biosphere–atmosphere exchange which significantly impacts tropospheric chemistry. Isoprene emissions from Chapman oak (Quercus chapmanii) grown for over two years in elevated CO₂ levels were measured and compared to emissions from trees grown in ambient CO₂ levels in identical open-topped chambers, and emissions from ambient-grown trees were compared to emissions from trees grown in chamberless control plots. Emission rates were adjusted to 30 μmol m⁻² s⁻¹ of light intensity and 30°C, and standard T-tests were performed to compare emission rates. No significant differences in isoprene emission were found in ambient vs. elevated CO₂ grown trees, while emissions from ambient vs. control trees showed a significant chamber effect.     

Compositional data analysis of smoke emissions from debris piles with low-density polyethylene

ABSTRACT

Data describing the composition of smoke are inherently multivariate and always non-negative parts of a whole. The data are relative and the information is contained in the ratios between parts of the composition. A prior analysis of smoke emissions produced from the burning of manzanita wood mixed with low-density polyethylene plastic applied traditional statistical methods to the compositional data and found no effect. The current paper applies compositional data techniques to these smoke emissions to determine if the prior analysis was accurate. Analysis of variance of the isometric log-ratios showed that LDPE significantly affected the CO₂ emission ratio for 8 of the 191 trace gases; this analysis showed none of the gases identified in the previous analysis were affected by LDPE. LDPE did not affect the CO₂ emission ratios for the alkanes, alkenes, alkynes, aldehydes, cycloalkanes, cycloalkenes, diolefins, ketones, MAHs, and PAHs. Compositional data analysis should be used to analyze smoke emissions data. Burning contaminant-free LDPE should produce emissions like wood.     

Analysis and modelling of the pollutant emissions from European cars regarding the driving characteristics and test cycles

Within the European research project ARTEMIS, significant works have been conducted to analyse the hot emissions of pollutant from the passenger cars regarding the driving cycles and to propose modelling approaches taking into account large but heterogeneous datasets recorded in Europe. The review and analysis of a large range of test cycles enabled first the building-up of a set of contrasted cycles specifically designed for characterizing the influence of the driving conditions. These cycles were used for the measurement of the pollutants emission rates from nine passenger cars on a chassis dynamometer.Emissions measured on 30 vehicles tested on cycles adapted to their motorization (i.e., cycles for high- or low-powered cars, inducing thus a significant difference in the dynamic) were also considered for analysing the influence of the cycles and of the kinematic parameters on the hot emission rates of the regulated pollutants (CO, HC, NO_x, CO₂, PM). An analyses of variance demonstrated the preponderance of the driving type (urban, rural road, motorway), of the vehicle category (fuel, emission standard) and emitting status (high/normal emitter) and thus the pertinence of analysing and modelling separately the corresponding emissions. It also demonstrated that Urban driving led systematically to high diesel emission rates and to high CO₂, HC and NO_x emissions from petrol cars. Congested driving implied high CO₂ (diesel and petrol) and high diesel NO_x emission. On motorway, the very high speeds generated high CO₂, while unsteady speeds induced diesel NO_x and petrol CO over-emissions. A search for pertinent kinematic parameters showed that urban diesel emissions were mostly sensitive to stops and speed parameters, while petrol emissions were rather sensitive to acceleration parameters. On the motorway, diesel NO_x and CO₂ emissions rates increased with the speed variability and occurrence of high speeds, while CO₂ and CO over-emission from petrol cars were linked to the occurrence of strong acceleration at high speeds.A modelling approach based on partial least square regression was tested, which demonstrates its ability to discriminate satisfactorily the emissions according to dynamic related parameters and in particular when considering the two-dimensionnal distribution of instantaneous speed and acceleration.Finally, a strategy was proposed to analyse the large but heterogeneous set of hot emission data collected within the ARTEMIS project. The approach consisted in analysing the similarity of the numerous cycles as regards their kinematic, grouping them into reference test patterns through an automatic clustering, and then computing reference emissions for these patterns. These principles enabled the development of a method to compute the emissions at a low spatial scale, i.e. the so-called traffic situation approach, which was implemented in the European Artemis model for estimating the cars’ pollutant emissions.