Most cities in China are experiencing severe air pollution due to rapid economic development and accelerated urbanization. Long-term air pollution data with high temporal and spatial resolutions are needed to support research into physical and chemical processes that affect air quality, and the corresponding health risks. For the first time, data on PM₁₀, PM_2.5, SO₂, NO₂, O₃ and CO concentrations in 23 ambient air quality automatic monitoring stations and routine meteorological were collected between January 2014 and December 2016 to determine the spatial and temporal variation in these pollutants and influencing factors in Chengdu. The annual mean concentrations of PM_2.5 and PM₁₀ exceeded the standard of Chinese Ambient Air Quality and World Health Organization guidelines standards at all of the stations. The concentrations of PM₁₀, PM_2.5, SO₂ and CO decreased from 2014 to 2016, and the NO₂ level was stable, whereas the O₃ level increased markedly during this period. The air pollution characteristics in Chengdu showed simultaneously high PM concentrations and O₃. High PM concentrations were mainly observed in the middle region of Chengdu and may have been due to the joint effects of industrial and vehicle emissions. Ozone pollution was mainly due to vehicle emissions in the downtown area, and industry had a more important effect on O₃ in the northern area with fewer vehicles. The concentrations of PM₁₀, PM_2.5, NO₂ and CO were highest in winter and lowest in summer; the highest SO₂ concentration was also observed in winter and was lowest in autumn, whereas the O₃ concentration peaked in summer. Haze pollution can easily form under the weather conditions of static wind, low temperature and relative humidity, and high surface pressure inside Chengdu. In contrast, severe ozone pollution is often associated with high temperature.

Farmland abandonment is considered as an important phenomenon for changing eco-environmental and sociocultural landscapes of mountainous rural landscape. Many studies have analyzed farmland abandonment, its driving factors, geophysical processes and consequences at landscape: however, very few have focused on mountainous developing countries such as in Nepal, which is a rapidly urbanizing country suffering from serious farmland abandonment. Therefore, our study was an attempt to (i) assess the spatiotemporal extent of farmland abandonment in Nepal, (ii) explore driving factors of farmland abandonment, and (iii) discuss on the eco-environmental and sociocultural consequences in Nepal. We reviewed various literature, documents, and national reports to obtain a dataset pertaining to the overall status of farmland use and changes along with political and socioeconomic changes, economic development processes, and policy and governance in Nepal. Our results showed that farmland abandonment is widespread; however, it is more prevalent in the hilly and mountainous regions of Nepal. A total of 9,706,000 ha, accounting for 23.9% of the total cultivated farmland in Nepal, was abandoned during the period of 2001 to 2010. The driving factors included population growth, scattered distribution of settlements, urbanization, socio-economic development, poor access to physical services, and poor implementation of agriculture development policies. Furthermore, the increasing extent of natural disasters, malaria eradication, land reform and resettlement programs, the complex system of land ownership, land fragmentation, political instabilities, and the intensification of trading in agricultural products also acted as drivers of farmland abandonment in Nepal. Farmland abandonment generates negative effects on rural societies eco-environmentally and sociologically. Abandoned plots were subjected to different forms of geomorphic damage (e.g. landslide, debris flows, gully formation, sinkhole development etc.). Farmland landscape fragmented into a group of smaller interspersed patches. Such patches were opened for grassland. Furthermore, farmland abandonment also has effects on the local population and the whole society in terms of the production of goods (e.g., foods, feed, fiber), as well as services provided by the multi-functionality (e.g. sociocultural practices, values and norms) of the agricultural landscape. Therefore, this study plays an important role in planning and implementing eco-environmental management and social development processes in Nepal.

The abandonment of farmland is a phenomenon that shows the deterioration of the physical and social landscape. It is widespread across the mountainous region of Nepal and is expected to further increase in the future. The aim of this study is to: (i) determine the driving factors of farmland abandonment; (ii) discuss its impact on social systems in terms of social networks, relations, practices; and (iii) assess the eco-environmental vulnerability of abandoned farmlands in the high mountain region of Nepal. The authors apply household surveys, focus group discussions and in-depth key informant interviews to visualize the social landscape and changes therein, thus establishing an existing history of mountain societies, social systems and management practices. Moreover, Google Earth Images and Low Height Remote Sensing Survey (UAV) are also used to observe geomorphic processes and the modification of the abandoned farmland and its characteristics. The Multivariate Linear Regression (MLR) model is run using SPSS revealing eight variables; mountain road accessibility, farm distance from residence, household size, household head age, landowner’s living place, ownership of farmland, salary and business and remittances are major factors determining the process of farmland abandonment in the region. Farmers’ social practices such as (i) an indigenous labor exchange system “Parma,” (ii) a traditionally managed irrigation system, (iii) a drinking water supply system, (iv) social practices, rituals, festive events, (v) an indigenous governance system, practices and some existing infrastructures (schools, banks, health, post, temples and more) are going to disappear as a process of abandonment. The decline in individual as well as social participation in land management practices have increased exotic vegetation and soil loss processes, modifying the physical characteristics of abandoned farmlands. Thus, this knowledge is vital for understanding appropriate social processes, natural resources and environmental management.

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region.