Role In Emissions Research Articles

Greenhouse gas emission prediction and impact analysis of dual-fuel engine

This study explored the profound influence of greenhouse gas (GHG) emissions on global climate change by introducing an innovative prediction model. Utilizing a backpropagation (BP) neural network with dual-hidden layer, optimized through simulated annealing particle swarm optimization (SA-PSO), the model predicted emissions from a diesel/natural gas dual-fuel engine at 1500 rpm across four torque levels: 400, 800, 1200, and 1600 N·m. The inputs included engine torque, injection timing, pressure, excess air coefficient, and natural gas substitution ratio, with CO2 and CH4 as outputs. Evaluation metrics—the coefficients of determination (R²) of 0.9975 for CO2 and 0.9951 for CH4, the root mean square error (RMSE) of 0.062 % and 278.04 ppm, and the mean relative error (MRE) of 0.82 % and 5.35 %, respectively—demonstrated the model’s accuracy. A quantitative analysis using the Mean Influence Value (MIV) algorithm showed engine torque’s pivotal role in emissions at a medium engine speed with contribution rates of 48.5 % and 40.3 % for CO2 and CH4 emissions, respectively. Notably, at a lower load condition (engine torque = 400 N·m), the natural gas substitution ratio was identified as having the most substantial impact on emissions. This study presents a novel approach to predicting and reducing GHG emissions from dual-fuel engines.

Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a Bayesian network risk assessment

With the increase in the number of natural disasters, caused by climate change, the role of emission and resilience has become more outstanding. In this paper, a novel model for quantifying and enabling the resiliency of a microgrid, using a Bayesian network risk assessment approach is presented. The three objective functions of cost, pollution, and resilience are optimized simultaneously using an improved lexicographic augmented ɛ-constraint method. It should be mentioned that these items are included in the proposed model due to the irreplaceable role of electric vehicles (EVs) and renewable energy sources (RESs) in distribution systems. Moreover, with the Bayesian network, the interaction between random variables is considered and a graphic view of casual relationships between stochastic and probabilistic variables is investigated in this study. The Monte Carlo scenario-based technique has handled the uncertainties of EVs and RESs. The proposed mixed integer linear programming (MILP) is implemented in the GAMS software environment under the CPLEX solver and finally, the results are discussed.

Evolutionary analysis of nuclear wastewater collaborative governance based on prospect theory

Dealing nuclear wastewater into the sea significantly threatens the global ecological environment and public health. Existing research primarily focuses on the roles of emission and regulatory bodies, often overlooking the collaborative interactions between these entities. This study extends traditional marine governance strategies by considering upstream and downstream countries as research subjects. We first establish an evolutionary game model for the collaborative governance of nuclear wastewater between upstream and downstream countries, utilizing the Prospect theory and Evolutionary Game Theory. Subsequently, numerical simulations are employed to explore different entities' decision-making behaviors and influencing factors. Our findings indicate that: (1) initial willingness affects the time it takes for the system to reach a stable state; (2) the benefits and costs of proactive governance strategies influence decision-maker's behaviors; (3) subjective factors (risk preference, loss aversion) affect the strategic choices. This research provides a scientific basis for understanding different countries' selection of nuclear wastewater governance strategies.

What’s Scope 2 good for?

Abstract The Greenhouse Gas Protocol’s Scope 2 has long been pivotal in addressing electricity's role in emissions, yet its effectiveness in emissions accounting is limited. This paper critiques Scope 2 for its inadequate allocation of electricity-derived emissions and for supporting misleading emissions reduction claims through market-based transactions. The paper investigates alternatives like 24/7 Carbon Free Electricity and Emissions First, noting their improvements over Scope 2, but also their failure to fully address grid embodied emissions. The concept of carbon solvency is introduced as a superior model, compelling firms to align long-term emissions liabilities with carbon removal assets, thus enabling more accurate emissions accounting and investment decisions for grid decarbonization. This approach fosters clearer decision-making, particularly in electricity generation methods with variable upstream emissions, such as hydrogen and biofuels. By redefining net zero within a rigorous, auditable framework, we propose a shift from current practices towards a more accountable and environmentally impactful model.

Controllable π-π coupling of intramolecular dimer models in aggregated states.

π-π coupling as a common interaction plays a key role in emissions, transport and mechanical properties of organic materials. However, the precise control of π-π coupling is still challenging owing to the possible interference from other intermolecular interactions in the aggregated state, usually resulting in uncontrollable emission properties. Herein, with the rational construction of intramolecular dimer models and crystal engineering, π-π coupling can be subtly modulated by conformation variation with balanced π-π and π-solvent interactions and visualized by green-to-blue emission switching. Moreover, it can rapidly respond to temperature, pressure and mechanical force, affording a facile way to modulate π-π coupling in situ. This work contributes to a deeper understanding of the internal mechanism of molecular motions in aggregated states.

Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel

This study aims to examine the energy use efficiency and conduct an environmental life cycle assessment (LCA) of the production of cumin and fennel using artificial intelligence (AI). The energy consumption for fennel and cumin is 34814.81 MJ ha−1 and 26214.17 MJ ha−1, respectively. The energy ratio for cumin is 0.66, indicating a substantial energy consumption per unit of output energy. Specifically, it takes 22.36 MJ of energy to produce 1 kg of cumin. Both crops exhibit a negative net energy balance. The results of the LCA analysis revealed three key findings when comparing the environmental emissions of cumin and fennel. Firstly, in the context of cumin cultivation, the environmental emissions related to human health were found to be 0.15 DALY. Secondly, there were no significant variations in the environmental emissions of medicinal plants when considering ecosystems. Finally, fennel production exhibited 237.77 USD2013 environmental publications in the resource category. It is worth noting that a substantial portion of the emissions in all three categories of medicinal plants can be attributed to direct emissions from crop cultivation. Additionally, nitrogen plays a significant role in emissions after the direct emissions occur. In comparison to fennel, cultivating cumin is recommended as it helps decrease environmental emissions and minimizes harm to water, air, and soil. The ANN model achieved a determination coefficient greater than 0.93 for four factors related to cumin cultivation in test mode. Similarly, the determination coefficient for output energy in fennel cultivation was found to be 0.852. However, the results indicate that the ANFIS test model outperformed the ANN model in terms of performance and accuracy. The ANFIS model exhibited an average detection coefficient of 0.95 for cumin cultivation and 0.93 for fennel cultivation, highlighting its superior performance compared to the ANN model. The application of AI techniques provides valuable insights into the production of cumin and fennel, facilitating informed decision-making towards more sustainable practices in the spice industry.

Bridging the spatial gaps of the Ammonia Monitoring Network using satellite ammonia measurements

Abstract. Ammonia (NH3) is a key precursor to fine particulate matter (PM2.5) and a primary form of reactive nitrogen. The limited number of NH3 observations hinders the further understanding of its impacts on air quality, climate, and biodiversity. Currently, NH3 ground monitoring networks are few and sparse across most of the globe, and even in the most established networks, large spatial gaps exist between sites and only a few sites have records that span longer than a decade. Satellite NH3 observations can be used to discern trends and fill spatial gaps in networks, but many factors influence the syntheses of the vastly different spatiotemporal scales between surface network and satellite measurements. To this end, we intercompared surface NH3 data from the Ammonia Monitoring Network (AMoN) and satellite NH3 total columns from the Infrared Atmospheric Sounding Interferometer (IASI) in the contiguous United States (CONUS) and then performed trend analyses using both datasets. We explored the sensitivity of correlations between the two datasets to factors such as satellite data availability and distribution over the surface measurement period, as well as agreement within selected spatial and temporal windows. Given the short lifetime of atmospheric ammonia and consequently sharp gradients, smaller spatial windows show better agreement than larger ones except in areas of relatively uniform, low concentrations where large windows and more satellite measurements improve the signal-to-noise ratio. A critical factor in the comparison is having satellite measurements across most of the measurement period of the monitoring site. When IASI data are available for at least 80 % of the days of AMoN's 2-week sampling period within a 25 km spatial window of a given site, IASI NH3 column concentrations and the AMoN NH3 surface concentrations have a correlation of 0.74, demonstrating the feasibility of using satellite NH3 columns to bridge the spatial gaps existing in the surface network NH3 concentrations. Both IASI and AMoN show increasing NH3 concentrations across the CONUS (median: 6.8 % yr−1 versus 6.7 % yr−1) in the last decade (2008–2018), suggesting the NH3 will become a greater contributor to nitrogen deposition. NH3 trends at AMoN sites are correlated with IASI NH3 trends (r = 0.66) and show similar spatial patterns, with the highest increases in the Midwest and eastern US. In spring and summer, increases in NH3 were larger than 10 % yr−1 in the eastern US and Midwest (cropland dominated) and the western US (pastureland dominated), respectively. NH3 hotspots are defined as regions where the IASI NH3 column is larger than the 95th percentile of the 11-year CONUS map (6.7 × 1015 molec.cm-2), they also experience increasing concentrations over time, with a median of NH3 trend of 4.7 % yr−1. IASI data show large NH3 increases in urban areas (8.1 % yr−1), including 8 of the top 10 most populous regions in the CONUS, where AMoN sites are sparse. A comparison between IASI NH3 concentration trends and state-level NH3 emission trends is then performed to reveal that positive correlations exist in states with strong agricultural NH3 emissions, while there are negative correlations in states with low NH3 emissions and large NOx emissions, suggesting the different roles of emission and partitioning in NH3 increases. The increases in NH3 could have detrimental effects on nearby eco-sensitive regions through nitrogen deposition and on aerosol chemistry in the densely populated urban areas, and therefore they should be carefully monitored and studied.

Microwave Emissivity of Typical Vegetated Land Types Based on AMSR2

To investigate the microwave radiation characteristics of different vegetation types, the “pure pixels” of 12 typical vegetated land types were selected and corresponding emissivity was retrieved under clear sky based on L1C AMSR2 observed brightness temperatures (TBs). According to the retrieved values for the 12 types, the spectral features in summer from 10.65 to 89 GHz were analyzed first. Then, the temporal variations in emissivity at 10.65, 18.7, and 36.5 GHz H-polarized (hereinafter 10H, 18H and 36H) are shown for the period from January 2018 to September 2020. Finally, the responses of 10H emissivity to surface skin temperature (SKT), the normalized differential vegetation index (NDVI), and soil moisture content (SMC) were quantitatively evaluated using a step-by-step analysis method. The general results are as follows: H-polarized (H-pol) emissivity increases with frequency and vegetation biomass, while the polarization differences decrease with frequency and vegetation biomass. The responses of V-pol emissivity to frequency and biomass are different from those of H-pol emissivity, and there are negative correlations with frequency and unusually high low-frequency values in grasslands and open shrublands (OS). The temporal variation amplitude of emissivity seems to be negatively correlated with vegetation biomass, and evergreen broadleaf forests show little variation. In general, the seasonal changes in emissivity are consistent with those of NDVI for most vegetation types. Nevertheless, in some cases, the change in emissivity is obviously ahead or behind that of NDVI, revealing that NDVI and emissivity may be sensitive to different vegetation elements that do not change in sync. In addition, variations in emissivity at different frequencies also show different amplitudes and turning points. Generally, the response of the 10H emissivity to SKT is weak, regardless of whether the response is positive or negative. The relatively large negative responses can be attributed to other indirect causes. NDVI plays a positive role in emissivity of the low-biomass vegetation in drier environments and medium- or high-biomass vegetation with clear seasonal variation. SMC is a complex factor that can have a positive or negative effect on emissivity.

The Influence of Ce, La, and SiC Particles Addition on the Formability of an Al-Si-Cu-Mg-Fe SiCp-MMC.

Road transport and the associated fuel consumption plays a primary role in emissions. Weight reduction is critical to reaching the targeted reduction of 34% in 2025. Weight reduction in moving parts, such as pistons and brake disc rotors, provide a high-impact route to achieve this goal. The current study aims to investigate the formability of Al–Si alloys reinforced with different fractions and different sizes of SiCp to create an efficient and lightweight Al-MMC brake disk. Lanthanum (La) and cerium (Ce) were added to strengthen the aluminium matrix alloy and to improve the capability of the Al-MMC brake discs to withstand elevated temperature conditions, such as more extended braking periods. La and Ce formed intermetallic phases that further strengthened the composite. The analysis showed the processability and thermal stability of the different material’s combinations: increased particle sizes and broader size range mixture supported the formation of the SiCp particle interactions, acting as an internal scaffolding. In conclusion, the additions of Ce and La strengthened the softer matrix regions and resulted in a doubled compression peak strength of the material without affecting the formability, as demonstrated by the processing maps.

High-Resolution Modelling of Carbon Dioxide Emissions Before and After the Implementation of a Designated Truck Lane

This work seeks to assess the impact of adding a lane for slower trucks on a divided multilane highway on CO₂ emissions. A portion of the U.S. 101 highway in San Luis Obispo County in California consists of the Cuesta Grade which is a 2.75-mile segment with a 7% grade. A microsimulation software, VISSIM, was used in conjunction with the Environmental Protection Agency’s emissions model, MOVES, to estimate CO₂ emissions on the corridor before and after the construction of the third lane. It was found that CO₂ emissions <i>decreased</i> between 1998 (before) and 2012 (after the 2003 lane addition), but the effect of the truck lane was shown to be different for the northbound (uphill) and southbound (downhill) directions. The truck lane in the northbound direction exhibited a 9.5% decrease in volume with 10.7% decrease in emissions, and the southbound direction experienced a 20.3% increase in volume but 7.4% decrease in emissions. For the northbound (uphill) direction, emissions seemed to correlate more closely with traffic volumes while a sensitivity analysis revealed travel speeds had a more profound effect on southbound (downhill) emission rates. In the conclusion section, ideas to further validate the emissions estimate are discussed. Emissions seemed to correlate more closely with traffic volumes (uphill) while travel speeds had a more profound effect on southbound (downhill) emission rates. One factor to be accounted for is the change in volume which seems to play a much larger role in emissions than roadway features or topography.

Types of planning systems and effects on construction material volumes: An explanatory analysis in Europe

The management of material stock is central for reducing emissions and for optimizing natural resource use. Construction materials are particularly the key because of their role in emission, resource use, and waste management concerns. However, the role of governance and spatial planning in the management of construction minerals is unclear. Based on a panel dataset of 35 European countries for 1995–2017, this study assesses the effect of governance and spatial planning factors, as well as of core socio-economic indicators (namely, GDP per capita, urbanization, and economic structure) at the national level According to the results, the types of spatial governance and planning systems significantly shape the effects on construction material use. Such effects range from − 67–215% relative to the most widespread system, i.e., the conformative system. Economic structure and urbanization have a positive feedback effect on the accumulation process at any level of development. In addition, the ability to resist excess material accumulation encoded by urbanization and economic transition is determined by spatial governance and planning systems. Additionally, a specific planning culture employing a moderate amount of construction material compared to other spatial governance and planning systems, i.e., the neo-performative system, is identified as a promising means to reduce significantly resource demand both in market- and state-led planning contexts.

Trends of nutrients and metals in precipitation in northern Germany: the role of emissions and meteorology

We analyzed the precipitation chemistry for a maritime region in northern Germany (Schleswig–Holstein) from 1997 to 2017 in order to reveal temporal and spatial patterns and to evaluate the role of meteorological factors relative to emission reductions in Germany and Europe. Therefore, we applied several statistical methods such as time series decomposition, principal component, and redundancy analysis. We extracted two main groups: (i) a marine group (Cl, Na, Mg) that was related to natural processes like sea spray input and (ii) an anthropogenic group (Pb, Cd, As, Zn, and nitrogen species) with a terrestrial subgroup (Fe, Al, Mn), which were both related to emissions. These groups were valid for the spatial, seasonal, and annual trend data. Other elements, like Ca, K, total P, and sulfate, were influenced by natural and anthropogenic processes. The seasonal variation of ammonium deposition was caused primarily by ammonia emissions and ancillary by precipitation. Most heavy metals as well as sulfate, nitrate, and ammonium showed decreasing trends in concentrations and deposition fluxes. Only Hg did not show any trend. The decreasing depositions of sulfate and total nitrogen were correlated to emission reductions in Germany. The deposition of most heavy metals was influenced by emission reductions on European scale and meteorological factors such as wind speed and humidity. Hg did not show any correlation with the emission time series in Europe. Instead, it was correlated to the NAO index and wind, implying that global emissions and transport pathways determine the temporal development of Hg depositions. Overall, the study reveals that emission reductions positively influence regional depositions for most investigated substances. The regional spatial patterns of depositions were also influenced by local meteorological factors.

Effects of Swirl and Boiling Heat Transfer on the Performance Enhancement and Emission Reduction for a Medium Diesel Engine Fueled with Biodiesel

In order to improve the accuracy of numerical simulation, a new heat transfer model is developed by using a modular approach in the Anstalt für Verbrennungskraftmaschinen (AVL)-Boost software. The improved heat transfer model mainly considers the effects of the swirl and boiling heat transfer inside the engine. In addition, a chemical kinetics mechanism including 475 reactions and 134 species is employed to predict the combustion of diesel engines fueled with biodiesel. The result shows that the boiling heat transfer will occur, especially in the high-temperature area. Analysis shows that the improved model is reliable and its precision is increased. Finally, the perturbation method is employed to investigate the relatively important inputs as the complex nonlinear function with a lot of output data and input data produced by the improved model. The relative effects of different parameters such as EGR, injection mass, injection timing, compression ratio, inlet air pressure, fuel injection pressure, exhaust pressure and inlet air temperature on performance and emission characteristics are compared. The eight parameters are investigated on four outputs of brake power, Brake Specific Fuel Consumption (BSFC), NOx and HC. The injected fuel mass plays an important role in emissions and performance. The EGR, compression ratio and inlet air pressure have a great effect on the HC and NOx emission.

The role of emissions and meteorology in driving CO2 concentrations in urban areas

A multi-year dataset of measurements of CO2 concentrations, eddy covariance fluxes, and meteorological parameters over the city centre of Florence (Italy) has been analysed to assess the role of anthropogenic emissions and meteorology in controlling urban CO2 concentrations. The latter exhibited a negative correlation with air temperature, wind speed, solar radiation, and sensible heat flux and a positive one with relative humidity and emissions. A linear and an artificial neural network (ANN) model have been developed and validated for short-term modelling of 3-h CO2 concentrations. The ANN model performed better, with mean bias of 0.58 ppm, root mean square error within 30 ppm, and r2=0.49. Data clustering through the self-organized maps allowed to disentangle the role played by emissions and meteorological parameters in influencing CO2 concentrations. Sensitivity analysis of CO2 concentrations revealed a primary role played by the meteorological parameters, particularly wind speed. These results highlighted that (i) emission reduction actions at local urban scale should be better tied to actual and expected meteorological conditions and (ii) those actions alone have limited effects (e.g. a 20% emission reduction would result in a 3% CO2 concentrations reduction). For all these reasons, large-scale policies would be needed.

Role of emissions and meteorology in the recent PM2.5 changes in China and South Korea from 2015 to 2018.

In this study, we examined the change rates of PM2.5 concentrations, aerosol optical depth (AOD), and the concentrations of PM2.5 precursors, such as SO2 and NO2, in China and South Korea using surface and satellite observations from 2015 to 2018. To quantify the impacts of the emissions and meteorology on the concentration changes, we performed a series of air quality simulations with year-specific meteorology and a fixed anthropogenic emissions inventory. The surface PM2.5 observations in China and South Korea decreased at rates of 9.1 and 4.3%/yr during the study period, respectively. The AODs from Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI) also decreased faster over China than the AODs over South Korea. For the PM2.5 decrease in China, the emission impact was more significant (73%) than the meteorology impact (27%). On the contrary, in South Korea, the emissions and meteorology impacts on PM2.5 reductions were similar (51% vs 49%). The SO2 concentration over China in 2018 significantly reduced to approximately half of the level in 2015. In turn, the sulfate concentration in Baengnyeong (BN), located in a downwind pathway from China to South Korea, decreased at a rate of 0.79%/month. However, the nitrate concentration in BN showed an increasing trend due to the non-linear chemical reactions among sulfate-nitrate-ammonium. The increased nitrate compensated for the reduced PM2.5 concentration from the sulfate decrease at BN. Additionally, the number of high (>50μg/m3) PM2.5 concentration days continuously decreased in China, but the number in South Korea increased. It is noted that emission reductions in an upwind area do not guarantee corresponding air quality improvement in the downwind area when complex secondary aerosol formation processes, as well as spatiotemporal changes in meteorology, are involved in the transboundary transport of air pollutants.

Demand contributors and driving factors of Singapore’s aggregate carbon intensities

Singapore is an island city state. It lacks conventional energy resources and is alternative energy disadvantaged. Yet its aggregate carbon intensity, given by the ratio of carbon emissions to GDP, dropped by 30% from 2005 to 2014. Currently it has a relatively low carbon intensity. Using I–O analysis, we study Singapore’s aggregate carbon intensity and the factors contributing to its changes at different levels, i.e. final demand, sector, and transmission levels. It is found that domestic exports accounted for nearly two-thirds of Singapore’s aggregate carbon intensity, followed by private consumption and investment. At the sectoral level, the top three contributors were the petroleum, petrochemical, and land transport sectors. Improvements in emission intensity and final demand structure were major factors contributing to reductions in carbon intensity. Our study contributes to the literature in two aspects. Methodologically, it deals with embodied emission intensity and analyzes its changes from the demand perspective. This differs from most studies in the literature from the production perspective. Empirically, the findings and policy implications are unique as they are applicable to a major city. With urbanization taking place in many developing countries, cites’ role in emissions and environmental sustainability are growing in importance but not many in-depth analyses similar to our study have been reported.

A study of cardiorespiratory related mortality as a result of exposure to black carbon

IntroductionAir pollution is a global phenomenon which invariably leads to a serious environmental and health related sequalae. “Black carbon” (BC), a subset of fine particulate matter ≤2.5 μm (PM2.5), is a fossil fuel emission by-product and has more recently been recognized as a major health hazard. The objective of this study is to statistically analyze the BC concentration and its correlation with cardiorespiratory related mortality and to estimate the benefits of BC reduction on the health of the population in the capital city of Tehran. MethodsWe analyzed the ambient air BC concentration and its correlation with cardiorespiratory related mortality and conducted health impact assessment of BC in Tehran (Jan 2018–Jan 2019). The data pertaining to BC concentration was obtained from Tehran's four major pollution monitoring stations. The mortality data was obtained from Tehran's cemetery registry. We calculated and analyzed BC concentration statistics including the mean, standard deviation, coefficient of variation, skewness, and kurtosis. We then assessed the cross-correlation and temporal relationship (0–7 days) between the daily mean concentration of BC for the entire city and cardiorespiratory related mortality. The BenMAP software was utilized to estimate the potential reduction in cardiorespiratory related mortality rates if BC concentration is reduced. Three hypothetical scenarios were employed in the analysis, utilizing the BenMAP software: (I) BC concentration was completely removed from the ambient air; (II) BC concentration was eliminated, and the remaining (non-BC portion of) PM2.5 concentration was reverted to the United States Environmental Protection Agency (EPA)’s standard level (i.e., 35 μg/m3); and (III) The BC emission during the night (22:00 h–6:00 h, when heavy-duty vehicles (HDVs) are allowed to commute in the city) was distributed throughout the whole day. Since the planetary boundary layer during daytime is much higher than that of nighttime, with the same rate of emission, lower concentrations are spread during the whole day. ResultsThe trend of BC concentration variation revealed a persistently higher emission of BC during the nighttime, which is consistent with the large-scale operation of HDVs during these hours in the city of Tehran. We observed a direct correlation between BC concentration and cardiorespiratory related mortality. Analysis also showed a 1.4-day lag period from the time of exposure to BC polluted air and respiratory related deaths, and 2 days for cardiovascular related deaths. As a result, the reduction in BC has significant beneficial effects in reducing potentially preventable cardiorespiratory related mortality. The aforementioned three scenarios for age groups of 30 and above yielded the following results: (I) 11,369 (126 per 100,000 population), (II) 15,386 (171 per 100,000 population), and (III) 2552 (28 per 100,000 population) potentially preventable all-cause (including cardiorespiratory) related deaths annually. ConclusionsThe BC concentration is relatively high in Tehran and HDVs have a major role in emission of this pollutant. A direct correlation between BC concentration and cardiorespiratory related mortality is observed. There are considerable health benefits in reducing BC concentration in this city. Our findings highlight the urgent need to actively curtail emissions of this harmful pollutant. This can be achieved through utilizing control mechanisms such as particulate filters or amending traffic laws.

Stain etching of silicon with V2O5 and FeCl3: Effect of etching time on photoluminescence

Visible photoluminescence (PL) was observed in porous silicon (pSi) layers etched by a solution of hydrofluoric acid containing Fe3+ and VO2+ ions. Stain films are much easier to produce than anodic etched films and require no special equipment. The role of structures reduced to nano range play a key role in emission of orange luminescence from the samples. Our study shows that the samples etched using VO2+ and Fe3+ oxidants exhibit stable orange luminescence under UV illumination. Also, etching for a particular time keeping same quantity of HF and different ratio of oxidant, a difference in peak was observed. The obtained morphological and optical results have been compared with the porous structure obtained after metal assisted etching on similar silicon substrates. The SEM images of samples fabricated with both the techniques have revealed that samples prepared with MACE results in more uniform order in structure formation.

Winter VOCs and OVOCs measured with PTR-MS at an urban site of India: Role of emissions, meteorology and photochemical sources

Within the outline of air quality studies at metropolitan city, the mixing ratios of seven selected volatile organic compounds (VOCs) were measured during December 2015 (winter) at an urban site of Pune. The measurement of VOCs was conducted using a proton transfer reaction-quadrupole mass spectrometer (PTR-QMS). The study represents daily variability of ambient VOCs and their various associated emission sources. Diurnal profiles have differed from one VOC to another as the result of their different origins and the influence of different meteorological parameters (i.e. solar radiation, temperature) and planetary boundary layer height (PBL-H). The hourly mixing ratios of Oxygenated-VOCs (OVOCs) and aromatics were in the ranges of 0.6–29 ppbv and 0.13–14 ppbv, respectively with OVOCs accounted for up to 75% of total measured VOCs. The role of long-range transport from the clear Thar Desert and polluted Indo-Gangetic Plain (IGP) was observed during the episodes of 1–15 and 17–31 December 2015, respectively. VOCs showed the strong diurnal variations with peaks during morning and evening hours and lowest in the afternoon. In the evening period, high levels of aromatics coincided with the lowest OVOCs suggests the role of fresh vehicular emissions. Emission ratios of various VOCs as a function of temperature showed the role of different sources including the biogenic and photochemical production as well as the anthropogenic sources, respectively. The higher emission ratio of Δmethanol/Δacetonitrile at the study site suggests the long range transport of biomass burning plumes from the Indo-Gangetic Plain (IGP) during the 17–31, Dec. 2015. In addition to the pattern of emission, the diurnal and day-to-day variations of VOCs were influenced by the local meteorological conditions and depth of planetary boundary layer (PBL-H).

Retrospective analysis of 2015–2017 wintertime PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; in China: response to emission regulations and the role of meteorology

Abstract. To better characterize anthropogenic emission-relevant aerosol species, the Gridpoint Statistical Interpolation (GSI) and Weather Research and Forecasting with Chemistry (WRF/Chem) data assimilation system was updated from the GOCART aerosol scheme to the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) 4-bin (MOSAIC-4BIN) aerosol scheme. Three years (2015–2017) of wintertime (January) surface PM2.5 (fine particulate matter with an aerodynamic diameter smaller than 2.5 µm) observations from more than 1600 sites were assimilated hourly using the updated three-dimensional variational (3DVAR) system. In the control experiment (without assimilation) using Multi-resolution Emission Inventory for China 2010 (MEIC_2010) emissions, the modeled January averaged PM2.5 concentrations were severely overestimated in the Sichuan Basin, central China, the Yangtze River Delta and the Pearl River Delta by 98–134, 46–101, 32–59 and 19–60 µg m−3, respectively, indicating that the emissions for 2010 are not appropriate for 2015–2017, as strict emission control strategies were implemented in recent years. Meanwhile, underestimations of 11–12, 53–96 and 22–40 µg m−3 were observed in northeastern China, Xinjiang and the Energy Golden Triangle, respectively. The assimilation experiment significantly reduced both high and low biases to within ±5 µg m−3. The observations and the reanalysis data from the assimilation experiment were used to investigate the year-to-year changes and the driving factors. The role of emissions was obtained by subtracting the meteorological impacts (by control experiments) from the total combined differences (by assimilation experiments). The results show a reduction in PM2.5 of approximately 15 µg m−3 for the month of January from 2015 to 2016 in the North China Plain (NCP), but meteorology played the dominant role (contributing a reduction of approximately 12 µg m−3). The change (for January) from 2016 to 2017 in NCP was different; meteorology caused an increase in PM2.5 of approximately 23 µg m−3, while emission control measures caused a decrease of 8 µg m−3, and the combined effects still showed a PM2.5 increase for that region. The analysis confirmed that emission control strategies were indeed implemented and emissions were reduced in both years. Using a data assimilation approach, this study helps identify the reasons why emission control strategies may or may not have an immediately visible impact. There are still large uncertainties in this approach, especially the inaccurate emission inputs, and neglecting aerosol–meteorology feedbacks in the model can generate large uncertainties in the analysis as well.