The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique (ARIMA-SVR). The main concept of generating a hybrid model is to combine different forecasting techniques so as to reduce the time-series forecasting errors. The data used in this study are annual CO2, CO, NOx, SO2, SO3, and SPM concentrations in Iran. According to the results, the ARIMA-TSVR Model is preferable over the other models, having the lowest error value among them which account for 0.0000076, 0.0000065, and 0.0001 for CO2; 0.0000043, 0.0000012, and 0.000022 for NOx; 0.00032, 0.00028., and 0.0012 for SO2; 0.000021, 0.000014, and 0.00038 for CO; 0.0000088, 0.0000005, and 0.00019 for SPM; and 0.000021, 0.000019, and 0.0044 for SO3. Furthermore, the accuracy of all models are checked in case of all pollutants, through RMSE, MAE, and MAPE value, with the results showing that the hybrid ARIMA-TSVR model has also been the best. Generally, results confirm that ARIMA-TSVR can be used satisfactorily to forecast air pollution concentration. Hence, the ARIMA-TSVR model could be employed as a new reliable and accurate data intelligent approach for the next 35 years’ forecasting.

Biodegradation assays involve both biodegradation and analytical processes which can be affected by systematic errors, among others. These errors can affect all the environmentally relevant parameters related to biodegradability, enantioselectivity (in the case of chiral compounds), kinetic parameters and persistence of chemicals. However, such impacts have never been well-characterized. In this work, calculations and models used for a long time are studied by simulating systematic errors at the 5% level, which affect independently the analytical calibration step and the biodegradation process. The impact of these errors is also compared with those obtained from an alternative approach: recently proposed equations and a novel model (a Monod modified version) developed in this work. All simulations are compatible with an environmentally relevant pollutant concentration. The results suggest a high degree of minimization (or even cancelation) of the systematic error impact using the alternative approach respect to the conventional one. These findings can be interpreted either in view of achiral or chiral pollutants. The present work can have a positive impact in the area of risk assessment of new pollutants and hazardous materials.

Estimates of marine debris are often based on beach surveys. Few studies have documented the veracity of these observations and the factors that may affect accuracy. Our laboratory-scale experiment identified potential sources of error associated with visual identification of marine debris (1–2 cm long) during shoreline surveys of sand beaches. Characteristics of the survey site (beach characteristics), observer (personal characteristics), and debris (color and size) may be important factors to consider when analyzing data from shoreline surveys. The results of this study show that the ability of individuals to accurately identify plastic fragments depends on the plastic and sand color, and density of shell fragments. Most suggestively, the high accuracy of blue plastic counts (95%) and the under-counting of white (50%) and clear plastic counts (55%) confirmed the hypothesis that a significant amount of clear and white plastic fragments may be missed during shoreline surveys. These results highlight the need for further research and possible modifications of visual shoreline survey methodologies in order to optimize this cost-effective method of marine debris monitoring.