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.

Study of Homogeneous Reservoir Pressure Inversion Model Based on Permeability Mechanics and Interpretation Software Design

Well test analysis is required during the extraction of oil and gas wells. The information on formation parameters can be inverted by measuring the change in wellbore pressure at production start-up or after well shutdown. In order to calculate the characteristic parameters of the well, this paper creates a well test interpretation model for homogeneous reservoirs based on the theory of seepage mechanics, uses the Stehfest–Laplace inversion numerical inversion algorithm, and builds the Gringarten–Bourdet logarithmic curves model. The model can be used to evaluate the homogeneous reservoir. We use this model to design the pressure inversion interpretation software to implement a pressure inversion method based on permeability mechanics theory by using computer. The software can obtain the reservoir characteristic parameters such as permeability ( K ), skin coefficient ( S ), and wellbore storage coefficient ( C ). The homogeneous formation Gringarten–Bourdet curves data are available at https://github.com/JXLiaoHIT/Study-of-homogeneous-reservoir-pressure-inversion-model.

Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas.

The Applicability of Remote Sensing Models of Soil Salinization Based on Feature Space

Soil salinization is a major challenge for the sustainable use of land resources. An optimal remote sensing inversion model could monitor regional soil salinity across diverse geographical areas. In this study, the feature space method was used to study the applicability of the inversion model for typical salt-affected soils in China (Yanqi Basin (arid area) and Kenli County (coastal area)), and to obtain soil salinity grade distribution maps. The salinity index (SI) surface albedo (Albedo)model was the most accurate in both arid and coastal regions with overall accuracy reaching 93.3% and 88.8%, respectively. The sensitivity factors for the inversion of salinity in both regions were the same, indicating that the SI-Albedo model is applicable for monitoring salinity in arid and coastal areas of China. We combined Landsat 8 Operational Land Imager image data and field data to obtain the distribution pattern of soil salinity using the SI-Albedo model and proposed corresponding countermeasures for soil salinity in the Yanqi Basin and Kenli County according to the degree of salinity. This study on soil salinity in arid and coastal areas of China will provide a useful reference for future research on soil salinity both in China and globally.

Long-term atmospheric emissions for the Coal Oil Point natural marine hydrocarbon seep field, offshore California

In this study, we present a novel approach for assessing nearshore seepage atmospheric emissions through modeling of air quality station data, specifically a Gaussian plume inversion model. A total of 3 decades of air quality station meteorology and total hydrocarbon concentration, THC, data were analyzed to study emissions from the Coal Oil Point marine seep field offshore California. THC in the seep field directions was significantly elevated and Gaussian with respect to wind direction, θ. An inversion model of the seep field, θ-resolved anomaly, THC′(θ)-derived atmospheric emissions is given. The model inversion is for the far field, which was satisfied by gridding the sonar seepage and treating each grid cell as a separate Gaussian plume. This assumption was validated by offshore in situ data that showed major seep area plumes were Gaussian. Plume total carbon, TC (TC = THC + carbon dioxide, CO2, + carbon monoxide), 18 % was CO2 and 82 % was THC; 85 % of THC was CH4. These compositions were similar to the seabed composition, demonstrating efficient vertical plume transport of dissolved seep gases. Air samples also measured atmospheric alkane plume composition. The inversion model used observed winds and derived the 3-decade-average (1990–2021) field-wide atmospheric emissions of 83 400 ± 12 000 m3 THC d−1 (27 Gg THC yr−1 based on 19.6 g mol−1 for THC). Based on a 50 : 50 air-to-seawater partitioning, this implies seabed emissions of 167 000 m3 THC d−1. Based on atmospheric plume composition, C1–C6 alkane emissions were 19, 1.3, 2.5, 2.2, 1.1, and 0.15 Gg yr−1, respectively. The spatially averaged CH4 emissions over the ∼ 6.3 km2 of 25 × 25 m2 bins with sonar values above noise were 5.7 µM m−2 s−1. The approach can be extended to derive emissions from other dispersed sources such as landfills, industrial sites, or terrestrial seepage if source locations are constrained spatially.

Retrieval of Chlorophyll a Concentration in Water Considering High-Concentration Samples and Spectral Absorption Characteristics

The envelope removal method has the advantage of suppressing the background spectrum and expanding the weak absorption characteristic information. However, for second-class water bodies with a relatively complex water quality, there are few studies on the inversion of chlorophyll a (Chl-a) concentration in water bodies that consider the spectral absorption characteristics. In addition, the current research on the inversion of the Chl-a concentration was carried out under the condition of sample concentration equilibrium. For areas with a highly variable Chl-a concentration, it is still challenging to establish a highly applicable and accurate Chl-a concentration inversion model. Taking Dongting Lake in China as an example, this study used high-concentration samples and spectral absorption characteristics to invert the Chl-a concentration. The decap method was used to preprocess the high-concentration samples with large deviations, and the envelope removal method was used to extract the spectral absorption characteristic parameters of the water body. On the basis of the correlation analysis between the water Chl-a concentration and the spectral absorption characteristics, the water Chl-a concentration was inverted. The results showed the following: (1) The bands that were significantly related to the Chl-a concentration and had a large correlation coefficient were mainly located in the three absorption valleys (400–580, 580–650, and 650–710 nm) of the envelope removal curve. Moreover, the correlation between the Chl-a concentration and the absorption characteristic parameters at 650–710 nm was better than that at 400–580 nm and 580–650 nm. (2) Compared with the conventional inversion model, the uncapped inversion model had a higher RP2 and a lower RMSEP, and was closer to the predicted value of the 1:1 line. Moreover, the performance of the uncapped inversion model was better than that of the conventional inversion model, indicating that the uncapped method is an effective preprocessing method for high-concentration samples with large deviations. (3) The predictive capabilities of the ER_New model were significantly better than those of the R_New model. This shows that the envelope removal method can significantly amplify the absorption characteristics of the original spectrum, which can significantly improve the performance of the prediction model. (4) From the inversion models for the absorption characteristic parameters, the prediction models of A650–710 nm_New and D650–710 nm_New exhibited the best performance. The three combined models (A650–710 nm&D650–710 nm_New, A650–710 nm&NI_New, A650–710 nm&DI_New) also demonstrated good predictive capabilities. This demonstrates the feasibility of using the spectral absorption feature to retrieve the chlorophyll concentration.