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.

Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R2: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.

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.

Under the background of growing greenhouse gas emissions and the resulting global warming, researches about the spatial-temporal variation analysis of the concentration of carbon dioxide in the regional and global scale has become one of the most important topics in the scientific community. Simulating and analyzing the spatial-temporal variation of the carbon dioxide concentration on a global scale under limited observation data has become one of the key problems to be solved in the research field of spatial analysis technology. A new research approach based on high accuracy surface modeling data fusion (HASM-DF) method was proposed in this paper, in which the output of the CO2 concentration of the GEOS-Chem model were taken as driving field, and the observation values of CO2 concentration at ground observation station were taken as accuracy control conditions. The new approach's objective is to fulfill the fusion of the two kinds of CO2 data, and obtain the distribution of CO2 on a global scale with a higher accuracy than the results of GEOS-Chem. Root mean square error (RMSE) was chosen as the basic accuracy index, and the experimental analysis shows that the RMSE of the result of the proposed approach is 1.886 ppm, which is significantly lower than that of the GEOS-Chem's 2.239 ppm. Furthermore, compared with the results created by the interpolation methods used the observation values at stations; the fusion results keep a good spatial heterogeneity similar to the results of GEOS-Chem. This research analyzed the spatial distribution and time series variation of the near-surface CO2 based on the fusion result on a global scale. And it can found that areas such as East Asia, Western North American, Central South America and Central Africa and other region show a relatively high value of the near-surface CO2 concentration. And we also found that the near-surface CO2 concentration changes with season, especially in North America and Eurasia, the near-surface CO2 in summer was significantly lower than winter in these areas.

The proposed and validated method for determination of ⁹⁰Sr content in environmental samples (water, soil and plant) is based on the radiochemical analytical separation of ⁹⁰Y from the sample and measuring its activity after the establishment of radioactive equilibrium with ⁹⁰Sr. Validation is the confirmation by examination and provision of objective evidence that they meet the individual requirements stipulated for a specific use. Validation of method was done based on the blank samples for water by adding ⁹⁰Sr known activity and using reference materials of soil (IAEA-326) and plant (IAEA-330). Content of ⁹⁰Sr in environmental samples was determined by α/β low level proportional counter. The accuracy and the precision of the applied method are confirmed and the method is validated and can be used for determination of ⁹⁰Sr in environmental samples. On the other hand, participations in interlaboratory comparisons are confirmed that the adequacy of the validated method is ensured.

Disposal of carbonless copy paper (CCP) paper sludge during the 1960s contaminated a site in the USA with PCBs. Despite historic records of CCP sludge disposal and absence of evidence of any other disposal, a dispute arose among the parties over the source of the PCBs. Aroclor 1242 is well documented as the PCB mixture used in CCP, yet Aroclors 1242, 1248, 1254, and 1260 were reported by the analytical laboratory. How could the PCBs at a single, small site be reported as four different Aroclors? Some claimed that there had to be at least four Aroclors source inputs to the site. Disposal of four different Aroclors at this site would simply defy logic and the historic record. Weathering of the mixtures is part of the story. A larger issue is the conflict between the intent of the USEPA 8082 method to determine the total PCB content in environmental samples to facilitate environmental cleanup and disposal decisions within a regulatory context versus the data users’ intent to identify the PCB sources. This inappropriate extension of the data leads to erroneous conclusions. To mitigate problems like this, laboratory analysis requests need to be matched to the intended data usage; conversely, the data must not be over-interpreted beyond the limits of the method. The PCB analysis community needs to develop a better articulation of the limits of Aroclor identification for the broader community that may naïvely assume that if the laboratory reports “Aroclor 1248,” then someone must have placed Aroclor 1248 at the site. After all, when a laboratory reports “lead” or “chloroform,” those identifications are never in question.