Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established PV power plants. However, a comprehensive map regarding the PV power plants' locations and extent remains scarce on the country scale. This study developed a workflow, combining machine learning and visual interpretation methods with big satellite data, to map PV power plants across China. We applied a pixel-based random forest (RF) model to classify the PV power plants from composite images in 2020 with a 30 m spatial resolution on the Google Earth Engine (GEE). The resulting classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km². We found that most PV power plants were situated on cropland, followed by barren land and grassland, based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants. The dataset of photovoltaic power plant distribution in China by 2020 is available to the public at https://doi.org/10.5281/zenodo.6849477 (Zhang et al., 2022).

The annual growth rate of pharmaceutical industry in Pakistan is 10% and is continuously expanding to fulfill the increasing demand of the rapidly growing population. But inability of the pharmaceutical sector to comply with the environmental standards leads to the introduction of large quantities of various pollutants in the natural environment which presents serious ecological challenges. In this study, effluent wastewater samples from 14 manufacturing units of the pharmaceutical industries of the National Industrial Zone, Rawat, Pakistan, were collected and characterized for physicochemical parameters including color, odor, pH, electric conductivity, temperature, total dissolved solids, total suspended solids, salinity, dissolved oxygen (DO), chemical oxygen demand (COD), nitrates, sulfates and phosphates according to the standard methods. The detection and quantification of diclofenac (DCF)—one of the commonly prescribed drugs in Pakistan—were carried out in the pharmaceutical wastewater samples (PWWSs) using HPLC-PAD. Exceptionally high concentration of the diclofenac was detected in the industrial disposal of MB-12 (311,495 µg L⁻¹). PWWSs were analyzed using chemometric techniques including principal factor analysis (PFA) and cluster analysis (CA). PFA explained almost 81.48% of the total variance by the newly extracted four components and complemented the strong Pearson’s correlation coefficient (r) of DCF concentrations to that of the levels of COD, r = 0.752, and DO, r = − 0.609, in PWWSs. Six clusters were generated during similar wastewater characteristics-based CA dendrogram, in which reverse osmosis-treated PWWSs were observed to cluster with the untreated PWWSs, suggesting the need to adopt an advance and better wastewater treatment methods by the pharmaceutical industries.

Satellites are an effective source of atmospheric carbon dioxide (CO₂) monitoring; however, city-scale monitoring of atmospheric CO₂ through space-borne observations is still a challenging task due to the trivial change in atmospheric CO₂ concentration compared to its natural variability and background concentration. In this study, we attempted to evaluate the potential of space-based observations to monitor atmospheric CO₂ changes at the city scale through simple data-driven analyses. We used the column-averaged dry-air mole fraction of CO₂ (XCO₂) from the Carbon Observatory 2 (OCO-2) and the anthropogenic CO₂ emissions provided by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) product to explain the scenario of CO₂ over 120 districts of Pakistan. To study the anthropogenic CO₂ through space-borne observations, XCO₂ anomalies (MXCO₂) were estimated from OCO-2 retrievals within the spatial boundary of each district, and then the overall spatial distribution pattern of the MXCO₂ was analyzed with several datasets including the ODIAC emissions, NO₂ tropospheric column, fire locations, cropland, nighttime lights and population density. All the datasets showed a similarity in the spatial distribution pattern. The satellite detected higher CO₂ concentrations over the cities located along the China–Pakistan Economic Corridor (CPEC) routes. The CPEC is a large-scale trading partnership between Pakistan and China and large-scale development has been carried out along the CPEC routes over the last decade. Furthermore, the cities were ranked based on mean ODIAC emissions and MXCO₂ estimates. The satellite-derived estimates showed a good consistency with the ODIAC emissions at higher values; however, deviations between the two datasets were observed at lower values. To further study the relationship of MXCO₂ and ODIAC emissions with each other and with some other datasets such as population density and NO₂ tropospheric column, statistical analyses were carried out among the datasets. Strong and significant correlations were observed among all the datasets.