This article presents results of research concerning the possibility of reducing the level of toxic nitric oxides (NOx) emission to the atmosphere. The research has been conducted on DKVR 20-13, PTVM-50 and DE 25-14 gas boilers. The complex character of this issue requires individual consideration regarding each boiler configuration. Each case requires consideration of characteristics and details of all elements constituting the boiler-furnace unit. The main problem was to establish the reference level to which the reduction of nitric oxides occurs. The actual maximum emission of nitric oxides was assumed as this level. It was verified with the maximum allowable emission of nitric oxides for each boiler. Three levels of the potential influence of emission on the atmosphere have been taken into account. This experimental research allowed for proposing an effective method, which led to reducing nitric oxides emission by around 30%.

In 2021, pak choi production in Indonesia was 727.47 Mg, marking an increase of 8.2% compared to the 2020 production, which was 667.47 Mg. Therefore, there is a clear need for cultivation improvement, particularly through the implementation of organic fertilisers. This study aimed to investigate the impact of liquid organic fertiliser (LOF) derived from fish waste and duck manure on the growth and yield of the pak choi plant ( Brassica rapa. L. var. Nauli F1). A randomised block design factorial was used with two factors and three replications. The first factor considered was LOF from fish waste, comprising three levels (LOF ₀ = control, LOF ₁ = 25 cm ³∙dm ^–3 of water, and LOF2 = 50 cm ³∙dm ^–3 of water). The second factor focused on duck manure fertiliser (DMF) and involved four levels (DMF ₀ = control, DMF ₁ = 3.7 kg∙plot ^–1, DMF ₂ = 5.55 kg∙plot ^–1, and DMF ₃ = 7.4 kg∙plot ^–1). The results showed that the application of LOF from fish waste positively influenced the growth and yield of pak choi, with the most effective treatment observed in LOF1 (25 cm ³∙dm ^–3 of water). However, the application of DMF did not yield a significant difference in its effect on the growth and yield of the pak choi plant. The control treatment (DMF ₀) reported comparable results and the combination of LOF from fish waste and DMF did not show a significant effect, with the most favourable findings observed in the LOF ₂DMF ₀ treatment (50 cm ³∙dm ^–3 and control).

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann–Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung–Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3) ₁₂ model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3) ₁₂, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas.