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Environmental risk has become an area of major concern and research, drawing special attention. This study on the environmental risk assessment (ERA) of Dar es Salaam Municipal Solid Waste comes at a time when the Government of Tanzania is becoming increasingly concerned about dealing with high levels of pollution from municipal solid waste (MSW). The paper employed the Driving force-Pressure-State-Impact-Response (DPSIR) model to establish an environmental risk indicator system and the analytical hierarchy process (AHP) to calculate and analyze risk values, based on the actual situation of MSW in the city of Dar es Salaam. It lists several measures that have been taken in response to the current significantly high levels of pollution, which have assisted in maintaining the environmental risk index (ERI) at a medium level (0.4–0.6) during the period from 2006–2017. However, these measures have not been adequate enough to manage the external pressure. The ERI has been increasing gradually, calling for timely formulation of demand-specific waste management policies to reduce the possibility of reaching the critical point in near future. With the use of the DPSIR model for ERA, this study has become highly valuable, providing empirical justification to reduce environmental risk from MSW, which is one of the main sources of environmental pollution in the urban areas of developing countries.

Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.

In this study, we used bands 7, 4, and 3 of the Advance Himawari Imager (AHI) data, combined with a Threshold Algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred on the China-Mongolia border regions, between 05:40 (UTC) on April 19th to 13:50 (UTC) on April 21st 2016. The results of the AHI data monitoring are evaluated by the fire point product data, the wind field data, and the environmental information data of the area in which the fire took place. The monitoring result shows that, the grassland fire burned for two days and eight hours with a total burned area of about 2708.29 km2. It mainly spread from the northwest to the southeast, with a maximum burning speed of 20.9 m/s, a minimum speed of 2.52 m/s, and an average speed of about 12.07 m/s. Thus, using AHI data can not only quickly and accurately track the dynamic development of a grassland fire, but also estimate the spread speed and direction. The evaluation of fire monitoring results reveals that AHI data with high precision and timeliness can be highly consistent with the actual situation.