Variations In Meteorological Conditions Research Articles

New insights from seasonal and weekly evolutions of aerosol absorption properties and their association with PM2.5 and NO2 concentrations at a central Mediterranean site

Aerosol absorption parameters were investigated in this study using the measurements performed by an aethalometer during a monitoring campaign from December 2021 to July 2022 in a suburban area in south-eastern Italy. The aerosol absorption coefficient was compared to both PM2.5 and NO2 concentrations from two nearby stations (a rural and an urban site). Different seasonal evolutions were identified for these extensive parameters, even if they also showed a common feature because all these parameters presented a higher variability in winter due to the highly variable meteorological conditions during that season. Intensive parameters like the aerosol absorption Ångström exponent and the PM2.5/PM10 ratio were also investigated showing the effects of wood burning from domestic heating and of vehicular traffic, especially both in the urban and in the suburban site. Then, the weekly evolution of both selected extensive and intensive parameters was also analyzed to identify potential impacts due to the weekly cycle of human activities. Most of the selected parameters presented a significant increase starting from Tuesday to Friday and then they generally decreased during the weekends due to the relevant reduction of the human activities. The selection of different types of monitoring sites (urban, suburban, and rural) and temporal scales (seasonal and weekly) has been proved for a proper characterization of the aerosol absorption properties at the monitored area due to its geographical location at the center of the Mediterranean area.

Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013–2022 in Anhui, China

The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 μg m−3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.

Pollution risk and life insurance decisions: Microgeographic evidence from the United Kingdom.

Recent research documents that exposure to air pollution can trigger various behavioral reactions. This article presents novel empirical evidence on the causal effect of pollution risk on life insurance decisions. We create a unique dataset by linking microgeographic air quality information to the confidential UK Wealth and Assets Survey. We identify an inverse N-shape relationship between pollution risk and life insurance adoption by exploiting the orthogonal variations in meteorological conditions. Over a given range above a threshold of exposure, rising pollution is associated with rising demand for life insurance, whereas at lower than the threshold levels of pollution, higher exposure risk reduces demand for insurance. Our findings indicate-for the first time-a nonlinear relationship between local pollution risk and life insurance demand.

Causes Investigation of PM2.5 and O3 Complex Pollution in a Typical Coastal City in the Bohai Bay Region of China in Autumn: Based on One-Month Continuous Intensive Observation and Model Simulation

In order to investigate the causes of complex air pollution in coastal cities in the Bohai Rim of China, a one-month intensive field observation combined with model simulation was carried out in a typical city named Dongying in September 2022. The results showed that two PM2.5 and O3 complex pollution episodes occurred in Dongying in the observation period, with the primary pollutant being O3. Atmospheric photochemical reactions occurring under unfavorable meteorological conditions led to the production of O3 while at the same time facilitating the generation of nitrate, sulfate, and other secondary components of PM2.5 by enhancing the atmospheric oxidizing capacity, which promoted the formation of complex air pollution. It was worth noting that in the context of high pollutants emission, the occurrence of complex air pollution was more sensitive to the variation in meteorological conditions than to the change in pollutants emission. To continuously improve air quality and protect human health in Dongying, it is recommended that an effective regional joint air pollution prevention and control mechanism with neighboring cities should be established in the premise of effective local pollutants reduction, and special attention should be paid to the adverse effect of the air mass transportation from Bohai Bay.

Techno-economic optimization of standalone photovoltaic-wind turbine-battery energy storage system hybrid energy system considering the degradation of the components

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well.

Division of the tropical savanna fire season into early and late dry season burning using MODIS active fires

Tropical savannas and grasslands are the most frequently burned biome in the world, and fire has an important role in sustaining ecosystem processes. Modern management of fires in savannas has roots in traditions stretching back centuries, and nowadays earth observation data is incorporated extensively in fire management practices. In tropical savannas in particular strongly seasonal monsoonal climates allow relatively low severity prescribed burning in the early part of the dry season (EDS) with the goal of preventing more destructive late dry season (LDS) fires. In many regional contexts it is common that a specific, fixed date is used officially to indicate when the window of safe burning has expired and the EDS transitions to the LDS, based on the experience of local or regional fire management authorities. This approach, while practical, neglects inter-annual variability in meteorological conditions and timing of onset of more dangerous fire weather. In this study, we pro-pose a remote sensing-based method for determining when this EDS window expires for five savanna-dominated continental-scale regions. By taking ad- vantage of the fact that conditions allowing night-time burning occur later in the dry season, we use day and night-time active fire detections from the MODerate Resolution Imaging Spectroradiometer (MODIS) instruments to set a flexible date of transition between the EDS and LDS. The vast majority of tropical savannas have very variable (std. dev. ≈ 20–40 days) transition dates, though this is somewhat modulated by fire frequency. Fuel connectivity rather than fuel condition appears to be a strong driving factor behind this variability. We find that especially national parks and protected areas have a high proportion of potentially more severe burning in the LDS, though areas with well-established EDS burning programmes are reducing this impact.

Exploring two-decadal risk variability of drought-flood abrupt alternation in a high-plateau basin

The escalating intensity and frequency of drought-flood abrupt alternation (DFAA) events, intensified by climate change, pose a significant threat to both the eco-environment and human life. Despite their evident impacts, the risk associated with DFAA events has received limited attention. In this study, we present a comprehensive risk assessment model incorporating multifold natural and anthropogenic indicators to analyze the spatiotemporal variations of DFAA risk. Utilizing a DFAA index (DFAAI) based on river flow data from seven monitoring stations spanning 1999 to 2019, we investigate DFAA events in the Dianchi Basin, the largest lake in the Yunnan-Guizhou Plateau, China. Our analysis reveals that 56% of total DFAA events occurred between June and August, with drought-to-flood (DTF) events accounting for 62.5% of DFAA occurrences in the Dianchi Basin. Notably, the intensity and frequency of DFAA events exhibit significant spatial variations across the basin. The hazard, vulnerability, exposure, and disaster defense indices further exhibit substantial spatial discrepancies, with hazard indicators being the most influential, notably the DFAA rate accounting for 72.4% of the overall impact. Spatial correlations in DFAA risk among monitoring stations were found to be low, primarily influenced by variations in meteorological conditions, underlying surface characteristics, and human activities. The findings of this study can offer valuable insights for effective water resource management in an ever-changing environment.

The skill of statistical downscaling in future climate with high-resolution climate models as pseudo-reality

Study regionBelgium Study focusWe assessed statistical downscaling (21 methods) skill and assumptions for seven extreme rainfall indicators in three Belgian areas. The ensemble consists of (i) perturbation methods with four members, (ii) quantile mapping with ten members, and (iii) machine learning with seven members. The methods were evaluated by a perfect predictor experiment in which the outputs of a high-resolution climate model are considered pseudo-observations. The ALARO-0 model with 4 km spatial resolution was considered the high-resolution climate model. The high-resolution outputs were compared with the downscaled results of three future scenarios of the 50 km resolution ALARO-0 model. New hydrological insightsThe methods’ skill depends on the area. The skill is higher for the Belgian coastal area compared to the inland and hilly areas. Methods which are based on stationary assumptions have a good skill for the coastal area but show a lower skill for the other areas where the temporal variability in meteorological conditions is stronger. The higher this temporal variability, the more prone are the stationary assumptions to deteriorate in time under different climate forcing conditions. The quantile-based perturbation methods are overall the most robust. An ensemble approach is advised, where different downscaling methods are applied and the uncertainty in the downscaling taken into account, especially when a pseudo-future evaluation, as done in this study, is not possible.

Analysis of the Influence of Variable Meteorological Conditions on the Performance of the EV Battery and on the Driving Range

The influence of variable weather conditions on the performance of the battery that powers electric vehicles (EV) was studied and analyzed. This paper also deals with the effects that changes in the performance of the battery have on the driving range of the vehicle. An algorithm to evaluate the influence of temperature on the behavior of the battery and on the real driving range of electric vehicles was developed. Our theoretical approach was assessed in experimental tests run under operating conditions that reproduce real situations. A correction factor was obtained to match theoretical and experimental values with an accuracy higher than 98%. A linear relation between driving range and ambient temperature was observed from a simulation process, with a high regression coefficient. The relation shows that the driving range increases with ambient temperature. The ratio of the estimated driving range from the simulation process and the standard value for a reference temperature of 25 °C was obtained. The ratio shows that the global driving range can be increased by up to 29% in high temperatures associated with the summer season, while for very low temperatures, near −30 °C, the global driving range is reduced by 20%. The comparative analysis of the driving range for different temperatures shows that there is a reduction of about 18% for the low range of ambient temperatures, between −15 °C and 5 °C, while for medium temperatures, between 5 °C and 25 °C, the reduction in the driving range is only 4.6%. Finally, tests demonstrated that with a reduction in high temperatures from 25 °C to 35 °C, the driving range only reduced by about 0.4%. For higher temperatures, around 50 °C, the longest driving distance can be achieved, with a higher accuracy.

Transport matrix of PM2.5 in Beijing-Tianjin-Hebei and Yangtze River Delta regions: Assessing the contributions from emission reduction and meteorological conditions

Both meteorological changes and emission control have significant impacts on PM2.5 concentration and regional transport contribution, where it is important to clarify the relationship among them for air pollution control. In this paper, the WRF-CAMx coupled with a set of simulation scenarios was employed for decomposing the contributions of meteorological conditions, emission, and regional transport considering transport matrix to PM2.5 concentration in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during 2013–2017. The results show that the local contributions occupied the primary position in PM2.5 concentrations in the BTH and YRD regions, reaching 57.9% and 58.1% in 2017, respectively. In terms of regional transports, they were mainly from Shandong Peninsula (SP) and Fen Nutrient Plain (FNP) for BTH, while Central triangle Region (CTR) and SP for YRD. Analysis of typical representative months suggested that in January, the concentration of local contribution reached peak. In addition, the results also show that when the intensity of emission reduction increased, the proportion of local contribution increased significantly while the local absolute contribution concentration decreased correspondingly. The meteorological conditions had little impact on transport matrix dominated by local contribution, whereas, the more favorable the meteorological diffusion conditions, the lower the contribution ratio of the out-of-region transport and the lower the contribution concentration. Moreover, the variation of meteorological conditions was more significant to the transport contribution outside the region. These findings can be helpful for formulating reasonable strategies of regional joint prevention and control and providing a valuable preference for other areas of China.

Research on Photovoltaic Power Prediction Based on BP Neural Network Algorithm

The output power of photovoltaic (PV) power plant is influenced by the fluctuation of meteorological conditions, which, coupled with the intermittent nature of the power output, has raised concerns about the stability of power grid. According to the variations in meteorological conditions, PV power prediction technology can predict the PV output power in advance to decrease the influence of PV power generation on the stable operation of the power grid. In this paper, the PV power prediction model is built on Matlab simulation software with a BP neural network algorithm. After that, the power prediction calculation of a PV power station is carried out under the operation conditions of sunny and cloudy days. The results show that, in the periods of prediction, the power curve of the predicted value is close to the power curve of the real value, which demonstrates the great accuracy of the BP prediction model established in this paper.

Aerosol Physical–Optical Properties under Different Stages of Continuous Wet Weather over the Guangdong–Hong Kong–Macao Greater Bay Area, China

The spatiotemporal distributions and physical–optical properties of aerosols are of great scientific significance for the study of climate change and atmospheric environment. What are the characteristics of aerosols in constant high humidity? Continuous wet weather (CWW) is a special weather phenomenon that occurs frequently during the late winter and early spring in South China. In this study, the CALIPSO satellite data and the ERA5 and MERRA-2 reanalysis data are used to analyze the aerosol optical properties of a total of 68 CWW processes from 2012 to 2021 in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA). We attempt to explore the variations in meteorological conditions and physical–optical properties of aerosols during the before-stage, wet-stage, and after-stage under different humidity levels. The results show that the prevailing wind direction is northeasterly and that the temperature and humidity are lower under the influence of cold high pressure in the before-stage. Moreover, the high aerosol optical depth (AOD) mainly results from regional transport. During the wet-stage, clean ocean airflow causes AOD to remain at a low level, whereas temperature and humidity increase significantly. The wet-stage ends with coldness when it is controlled by cold high pressure again. The atmospheric circulation in the after-stage is similar to that in the before-stage. However, a remarkable feature is that there is a temperature and humidity inversion layer, which results in a significant increase in AOD. This study reveals the physical–optical properties of aerosols during the three stages and the influence mechanism of meteorological factors on aerosols, which can provide a scientific basis for the study of CWW in the future.

Spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors in the Niger River Watershed, West Africa

Identifying the main factors influencing PM2.5 concentrations is critical for the control and management of elevated atmospheric pollution. Although the impact of natural and land use factors on PM2.5 concentration has previously been quantified, in this paper we show that multiple environmental factors exhibit significant spatial heterogeneity at a range of scales. A more comprehensive analysis of PM2.5 variations require the potential effects of seasonal variations in meteorological conditions, interactions between natural and land use factors, and possible multi-scale spatial effects to be accounted for. Based on the global remote sensing inversion dataset, the aim of this study is to elucidate the spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors, and their multi–scale effects. This is achieved through combining geographical detector (GD), random forest (RF) and multiscale geographically weighted regression modelling (MGWR) to reveal the underlying mechanisms of spatial variation of PM2.5 concentrations in the Niger River Watershed (NRW). The results show that natural factors, represented by meteorological and topography variables, are most prominent. Human activities, represented by land use, have significant indirect effects through interactions with meteorological factors. Elevation (ELE) and annual minimum temperature (TMIN) emerge as the most important interaction centers among natural factors. In addition, the interaction between natural and land use factors are crucial in considering PM2.5 transmission and dispersion in the NRW. The topography and meteorological factors underlie the basic spatial pattern PM2.5 concentrations in the NRW, among which seasonal meteorological parameters have a more prominent impact on PM2.5 concentration. Both natural and land use factors exhibit strong non–stationarity in respect of spatial variations in PM2.5, and the effect of environmental factors in general on PM2.5 is scale–dependent. Transport and dispersion are important constraints in terms of aerosol loading regionally. The study suggests that coupling processes between meteorological conditions, land use and PM2.5 concentrations, based on the scale effects of each factor, should be accounted for in developing aerosol pollution regulations and cooperative management strategies in the region.

Analysis of 222Rn Surface Concentrations in the Basque Country (Spain): A Case Study of Heat Waves.

The objective of this study was to characterize radon concentrations registered in the Radiological Surveillance Network of the Basque country in relation to local meteorological parameters, and to determine its behaviour under heatwave events. For this purpose, radon measurements and meteorological parameters from June 2012 to June 2015 were analysed at two sites, Bilbao and Vitoria (northern Spain), in a region characterized by complex orography, causing large temporal and spatial variability in meteorological conditions. Yearly, seasonal, and diurnal cycle differences and similarities were investigated at both sites. The temporal evolution of radon concentration was analysed at both sites during the two heatwave periods officially identified by the State Meteorological Agency (8-11 August 2012 and 17-23 August 2012). The analysis revealed two different patterns of radon concentrations, in terms of both time and intensity, under this synoptic pattern, making it also possible to identify regional transport channels of radon concentrations between the two sites. This set of results evidences the adequate position of both stations to represent the spatial and temporal evolution of radiological variables continuously in this region.

Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables

AbstractSnowpack water equivalent (SWE) is a key variable for water resource management in snow‐dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long‐term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra‐ and inter‐year variability in meteorological conditions on densification through a cumulative degree‐day index derived from North American Regional Reanalysis products. A non‐linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross‐validation, the predicted densities had a root‐mean‐square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash‐Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression‐based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure‐from‐Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.

Assessment of dynamic line rating forecasting methods

• Forecasting electric power transmission line ampacity is presented here to be pivotal to achieving an increased line rating. • Deep learning techniques and regression were used to identify patterns in the environmental data and forecast DLR at different lead times. • This article compared forecasting techniques and evaluated them with point and probabilistic error metrics. • Point Error metrics gave the discrepancy between the forecasted and actual values, while the probabilistic error metrics gave the confidence level of the forecast values of the techniques. Optimal transmission line rating use is guaranteed with dynamic line rating (DLR). It is a smart grid technology that foresees variations in meteorological conditions affecting line rating and deploys algorithms to effect changes to the line rating due to these conditions. Electric power system operators use forecasted DLR for system planning, operation, and delivery. This study reviewed DLR forecasting techniques, classified them, implemented them, and compared their outputs at different lead times. It used ensemble means forecasting, recurrent neural network (RNN), and convolution neural network (CNN). Ensemble forecasting technique deployed in this study involves a Monte-Carlo simulation that produces random, equally viable predicting solutions. Alternatively, a neural network layer's initial outcome is fed back into it to predict the output in RNN, while CNN learns to predict features that vary in time and space with marginal discrepancies. This study used quantile regression (QR), ensemble forecasting, RNN and CNN to forecast DLR at 12hrs, 24hrs and 48hrs. The tested forecasting approaches prove efficient, but ensemble forecasting seems less error-prone, more secure and conservative among all methods. On average, 75th percentile quantile regression and ensemble forecasting demonstrate better reliability and avail us the better choice of ampacity among the forecasting techniques.

AN INITIAL DATA-LIMITED MODELING OF THE ENVIRONMENTAL CONSEQUENCES: CASE-STUDY OF THE VASYLKIV FUEL RESERVOIR FIRE

The paper presents the application of the Multi-Criteria Evaluation of environmental damage under the conditions of limited available data. War actions often cause damage to industrial facilities, which in turn impacts the environment. At the same time, access to such sites and information about the development of specific events may be limited or fragmented. To support the decision-making process in such situations, the Multi-Criteria Evaluation offers structured and transparent utilization of the known quantitative and qualitative information. The Vasylkiv fuel depot fire in Kryachki village during the early days of the war was analyzed in terms of potential damage to soil, which is often omitted in the assessments of the environmental impacts of fire. The case-study analysis included a definition of the “fire-environment” system components and the factors affecting the final level of damage, the weighting of these factors and formulation of the trends describing the intensity of soil pollution as a product of particular factor values. The set dependencies were then used to model scenarios with variable meteorological conditions and varied infrastructural conditions of the reservoir park. The modelling results imply the need to account for meteorological parameters in the evaluation of environmental damage and the development of post-accident mitigation plans. The Multi-Criteria Evaluation is also recommended for preparing for potential accidents since it can compensate for the lack of data through theoretical knowledge and practical experience if a multidisciplinary team is involved.

Assessing the impact of synoptic weather systems on air quality in Sydney using Radon 222

The state of the atmosphere can affect the degree of dilution of emitted pollutants, and similar local and diurnal meteorological conditions can be grouped according to prevailing synoptic scale weather systems. Traditionally, various methods have been used to classify atmospheric conditions. Here we investigate the use of Radon-222 (radon) for the classification of weather systems and determine the degree of dilution under different synoptic conditions.Air quality data was analyzed in Richmond Australia over a 13-year period. Radon was used to determine 5 nocturnal stability categories numbered 0 to 4, with 0 capturing times of fast-changing fetch influences, and 1–4 representing increasing conditions of nocturnal stability (1: near neutral, to 4: strongly stable). These categories were then related to prevailing synoptic weather systems. It was found that increasing radon-derived stability categories were associated with reducing intensity of frontal systems traversing southern Australia and extending into the Southern Ocean, with stability category 4 associated with blocking high pressure systems located to the east of the site.The median nocturnal wind speeds and mixing layer heights decreased as we moved from stability category 0 to 4, indicating that the mixing volumes for pollutants released into the atmosphere from surface sources was progressively getting smaller. This decreasing dilution from stability category 0 to 4 resulted in strongly increasing median pollutant concentrations. However, on diurnal and seasonal time scales, differences were observed which could be attributed to changes in emission rates.Our results show that when investigating the impact of air quality mitigation strategies, any variations in meteorological conditions during the comparison periods needs to be considered in addition to changes in pollution emission rates, and that near-surface radon observations and the ventilation coefficient are a convenient and effective tool for this purpose.

Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO

Abstract. Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at the regional to city scale. Very few studies have focused on combustion efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for interpreting and deriving spatially resolved ERs from asynchronous satellite measurements, including (1) variations in meteorological conditions given the mismatch in satellite overpass times, (2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), (3) interferences from the biosphere and biomass burning, and (4) the mismatch in the daytime variations of CO and CO2 emissions. In this study, we extended an established emission estimate approach to arrive at spatially resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influences of the confounding factors listed above and further attribute intra-urban variations in ERs to certain sectors, we leveraged a Lagrangian atmospheric transport model with an urban land cover classification dataset and reported ERCO values from the sounding level to the overpass and city level. We found that the differences in overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially resolved ERCO. Specifically, a time difference of >3 h typically led to dramatic changes in wind directions and urban plume shapes, thereby making the calculation of accurate sounding-specific ERCO difficult. After removing such cases from consideration and applying a simple plume shift method when necessary to account for changes in wind direction and speed, we discovered significant contrasts in combustion efficiencies between (1) two megacities versus two industry-oriented cities and (2) different regions within a city, based on six nearly coincident overpasses per city. Results suggest that the ERCO impacted by heavy industry in Los Angeles is slightly lower than the overall city-wide value (<10 ppb-CO/ppm-CO2). In contrast, the ERCO related to heavy industry in Shanghai is much higher than Shanghai's city mean and more aligned with the city means of two selected industry-oriented cities in China (approaching 20 ppb-CO/ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our unique approach (i.e., without using sector-specific information from emission inventories) provides new insights into assessing combustion efficiency within a city from future satellite missions, such as those that will map column CO2 and CO concentrations simultaneously with high spatiotemporal resolutions.

Photovoltaic System Parameter Estimation Using Marine Predators Optimization Algorithm Based on Multilayer Perceptron

Solar and wind based renewable energy sources are highly variable according to unstable energy sources due to variable meteorological conditions. Providing a stable DC bus voltage to the inverter’s input by eliminating voltage fluctuations in the generated electrical energy is the main concern of researchers and is highly sensitive in the integration of distributed energy sources. In this study, in order to provide stable and sustainable output voltage at the output of a DC/DC converter, parametric simulation studies are carried out according to different input voltage, duty ratio, and switching frequency values of a DC/DC power converter circuit in a system fed with a solar energy source. Using this dataset obtained from a solar energy source, a parameter estimation in the smart grid structure is studied using artificial hybrid prediction intelligence techniques. Grey wolf and marine predators optimization algorithms are integrated to the multilayer perceptron in the stage of developing the hybrid prediction models. Then, the accuracy of the prediction processes is tested and verified with the power electronics circuit software. Thus, a data analysis approach based on parametric simulation studies in smart grid structures is reported in order to give clear ideas to researchers before designing a prototype.