Spatial Correlation Network and Driving Effect of Carbon Emission Intensity in China’s Construction Industry

To explore the spatial network structure characteristics and driving effects of carbon emission intensity in China's construction industry, the investigation combined the modified gravity model and social network analysis method to deeply analyze the spatially associated network structure characteristics and driving effects of carbon emission intensity in China's construction industry, based on the measurement of carbon emission data of China's construction industry from 2006 to 2017. The results show that the regional differences of carbon emission of construction industry are significant, and the carbon emission intensity of construction industry show a fluctuation trend. The overall network of carbon emission intensity shows an obvious “core-edge” state, the hierarchical network structure is gradually broken. Economically developed provinces generally play a leading role in the network, and play an intermediary role to guide other provinces to develop together with them. Among the network blocks, most of the blocks play the role of “brokers”. The block with the leading economic development has a strong influence on the other blocks. The increase of network density, the decrease of network hierarchy and network efficiency will reduce the construction carbon emission intensity.

A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence

By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.

Long-Range Transport of Sulfur Dioxide Emissions From External Sources to Tehran

Large-scale emissions of sulfur dioxide (SO2) from the combustion of heavy fuel oils are deteriorating the air quality in Tehran and regularly causing complex atmospheric pollution situations and human health concerns. Our analysis of the long-term SO2 emission data in Tehran confirmed that the magnitude of local SO2 emission sources is not adequate to reach SO2 concentrations to their present levels. Tehran is predominantly affected by regional transport of SO2 from exterior sources further away located in Iraq, Saudi Arabia, and adjacent provinces neighboring Tehran. Approximately 80% of total SO2 emissions in Tehran were observed to have impacts from the external hotspots outside of Tehran. While local emission sources only contribute around 20% of the total SO2 emissions. Bivariate polar plots, k-mean cluster, pairwise polar correlation, and PSCF analysis provided evidence for the impact of large-scale transport of SO2 emissions from external locations from the west/northwest, north/northeast, and south/southwestern areas of the region. Further observations of these hotspot areas observed in our analysis with TROPOMI satellite data confirmed significant SO2 emissions resulting from the consumption of heavy fuel oils in thermal power plants and oil/gas refineries. Overall, the results suggested that the regulatory strategies for controlling local traffic emissions of SO2 in Tehran would not be beneficial for reducing public health exposures to SO2 in Tehran. Such improvements can be attained mainly by diminishing the emission sources located further away from Tehran.

Vehicle Traffic as a Source of CO2, CO, HC and Particulate Polycyclic Aromatic Hydrocarbon in the Mingora City, District Swat, KP, Pakistan and Their Effect on Health, Temperature and Tourism

In this work we collected a large number of vehicular emission data of carbon dioxide (CO2), carbon monoxide (CO) and hydrocarbons (HC) from custom paid and non-custom paid vehicles in the Swat district, which are responsible for changing the climate and global warming. Swat valley is facing severe threats and impacts of the climate change as there is a record high increase in the temperature with flash floods and droughts becoming increasingly common. The main cause of the increasing warm weather is vehicle emissions along with the cutting of forest on a large scale in the valley. Hospital records for 2768 children aged 0 to 18 years (697 of whom had two encounters) were obtained for a main city area of two hospitals in Saidu Sharif, Swat. Residential addresses were geocoded. A line source dispersion model was used to estimate individual seasonal exposures to local traffic-generated pollutants (nitrogen oxides, carbon monoxide and hydroxide).

Charge transfer state characterization and voltage losses of organic solar cells

A correct determination of voltage losses is crucial for the development of organic solar cells with improved performance. This requires an in-depth understanding of the properties of interfacial charge transfer (CT) states, which not only set the upper limit for the open-circuit voltage of a system, but also govern radiative and non-radiative recombination processes. Over the last decade, different approaches have emerged to classify voltage losses in organic solar cells that rely on a generic detailed balance approach or additionally include CT state parameters that are specific to organic solar cells. In the latter case, a correct determination of CT state properties is paramount. In this work, we summarize the different frameworks used today to calculate voltage losses and provide an in-depth discussion of the currently most important models used to characterize CT state properties from absorption and emission data of organic thin films and solar cells. We also address practical concerns during the data recording, analysis, and fitting process. Departing from the classical two-state Marcus theory approach, we discuss the importance of quantized molecular vibrations and energetic hybridization effects in organic donor-acceptor systems with the goal to providing the reader with a detailed understanding of when each model is most appropriate.

Development of damage detection parameters over the lifetime of a rolling element bearing

This paper presents the trends of damage detection parameters over the lifetime of a rolling element bearing. In the experimental part, a series of bearing tests was performed using the twin-disc test device, until the monitored bearing was severely worn. This was followed by the analysis of measured acceleration and acoustic emission data in a constant-load condition, but also as loaded with impact-type loading. The results showed that traditionally used parameters, such as kurtosis and RMS, can indicate whether the bearing is damaged or not in a non-impact load condition. However, especially under impact-loading, the parameters based on acoustic emission data showed good performance and enabled monitoring of progress of the bearing damage.

Stability of Insulin on Polycaprolactone Nanoparticles as a Function of Surface Properties

The purpose of this research was to formulate insulin-loaded polycaprolactone (PCL) nanoparticles and evaluate structural stability of the protein using fluorescence spectroscopy. The size and morphology of the nanoparticles were characterized using dynamic light scattering (DLS) and scanning electron microscopy (SEM). Fluorescence emission data revealed that insulin is most stable with multilayer adsorption at pH close to its isoelectric point (IEP). The obtained particle size ranged from 130-140 nm+22 (SD). The loading amount of insulin onto the PCL nanoparticles was low at pH 7.4 and relatively high at pH 5.3. The adsorption phenomenon of protein onto hydrophobic nanoparticles provides a promising noninvasive carrier system for insulin.

AN APPLICATION OF MACHINE LEARNING TO SHIPPING EMISSION INVENTORY

The objective of this study is to develop a shipping emission inventory model incorporating Machine Learning (ML) tools to estimate gaseous emissions. The tools enhance the emission inventories which currently rely on emission factors. The current inventories apply varied methodologies to estimate emissions with mixed accuracy. Comprehensive Bottom-up approach have the potential to provide very accurate results but require quality input. ML models have proven to be an accurate method of predicting responses for a set of data, with emission inventories an area unexplored with ML algorithms. Five ML models were applied to the emission data with the best-fit model judged based on comparing the real mean square errors and the R-values of each model. The primary gases studied are from a vessel measurement campaign in three modes of operation; berthing, manoeuvring, and cruising. The manoeuvring phase was identified as key for model selection for which two models performed best.

Characteristics, Decoupling Effect And Driving Factors of Regional Tourism's Carbon Emissions In China

Using the tourism's carbon emission data of 30 provinces (cities) in China from 2007 to 2019, we have established a logarithmic mean Divisia index (LMDI) model to identify the main driving factors of carbon emissions related to tourism and a Tapio decoupling model to analyze the decoupling relationship between tourism's carbon emissions and tourism-driven economic growth. Our analysis suggests that China's regional tourism's carbon emissions are growing significantly with marked differences across its regions. Although there are observed fluctuations in the decoupling relationship between regional tourism's carbon emissions and tourism-driven economic growth in China, the data suggest weak decoupling. Nonetheless, the degree of decoupling is rising to various extents across regions. Three of the five driving factors investigated are also found to affect on emissions. Both tourism scale and tourism consumption lead to the growth of tourism's carbon emissions, while energy intensity has a significant effect on reducing emissions. These effects differ across regions.

Fugitive Emission Monitoring System Using Land-Based Sensors for Industrial Applications

Metal-oxide based emission detection sensors are typically used for point measurements of hydrocarbon emissions. They are low-cost sensors and can be used for continuous monitoring of emissions. This paper describes an analytical framework that uses time series data from a collection of such sensors deployed at a customer site, along with weather conditions, to detect anomalies in emission data, identify possible emission sources and estimate the leak rate from fugitive emissions. The analytical framework also comprises an optimization module that helps in determining the optimal number of sensors required and their potential location at a customer site. The paper discusses results of the different steps in the analytical framework obtained using concentration data generated using numerical simulations and obtained through controlled leak field tests.