Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.     

Forecasting hourly PM<Subscript>10</Subscript> concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM₁₀ concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R ² values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R ² values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.     

Evaluation of the mobility of c14‐labelled pesticides in soils by thin layer chromatography using a linear analyser

Abstract

The mobility of seven pesticides in a chromic cambisol soil was studied by soil thin layer chromatography. Pesticide mobilities were determined by means of conventional autoradiographs of the chromatograms, as well as from sequential series of curves and images of the pesticide spots provided by a linear analyser. The R _f values obtained from the autoradiographs and those provided by the linear analyser were quite consistent. Based on such values, pesticide mobility decreased in the following order: acephate > fluometuron > atrazine > ethofumesate > metolachlor > diazinon > glyphosate. According to the mobility scale proposed by Helling and Turner (1968), acephate is highly mobile; atrazine, fluometuron, ethofumesate and metolachlor are moderately mobile; diazinon is slightly mobile; and glyphosate is immobile. The images provided by the linear analyser allow to determine the R _f values for the zones of maximum activity in the pesticide spots (R _f ^max), as well as the activities of different spot zones and those corresponding to R _f and R _f ^max. The results obtained show the image analyser to provide more expeditious R _f measurements from the chromatograms and open up new prospects for using soil TLC to investigate pesticide mobility.     

A two-step approach for relating tapered element oscillating microbalance and dichotomous air sampler PM2.5 measurements

A method for transforming continuous monitoring (CM) fine particulate matter (aerodynamic diameter <2.5 μm; PM_2.5) data (i.e., by tapered element oscillating microbalance [TEOM]) obtained from the Canadian National Air Pollution Surveillance (NAPS) program to meet the data quality objective (DQO) of R² > 0.8 against the co-located federal reference method (i.e., dichotomous air sampler) is described. By using a two-step linear regression to account for the effect of the ambient temperature, 16 out of the 23 examined sites met the common model adequacy threshold of R² > 0.8. After the transformation, 20 out of the 23 examined sites met the DQO of R² > 0.7, as recommended by the U.S. Environmental Protection Agency (EPA). A combined two-step statistical approach was also examined and revealed similar results. The methods described herein show that the CM data can be successfully transformed to meet DQOs for representative sites across Canada using year-round (both summer and winter) data.

Implications:This study provides a transformation approach to correct ambient TEOM data against the federal reference method without dividing the ambient data according to warm and cold seasons. This transformation approach will significantly improve the correlation coefficient between TEOM and dichotomous air sampler data. It is possible that TEOM data at many Canadian locations can be transformed to meet the EPA data quality objective, thus making this transformation approach useful for comparisons of ambient PM data across jurisdictions.     

A Simple Technique for Stack Design in Complex Terrain

The main objective of this study is to apply neutral electrolyzed water (NEW) spraying to inactivate bioaerosols. We evaluated the inactivation efficiency of NEW applied to inactivate two airborne bacterial Escherichia coli and Bacillus subtilis aerosols inside an environmental-controlled chamber in the study. Generated with electrolyzing 6.15 M sodium chloride brine, the NEW with free available chlorine (FAC) concentration 50, 100, and 200 ppm was pumped with an air pressure of 70 kg/cm² through nozzle into the chamber to inactive E. coli and B. subtilis aerosols precontaminated air (initial counts of 3?×?10⁴ colony-forming units [CFU]/m³). Bacterial aerosols were collected and cultured from chamber before and after NEW spray. The air exchange rate (ACH, hr^?1) of the chamber was set to simulate fresh air ventilating dilution of indoor environment. First-order concentration decaying coefficients (Ka, min^?1) of both bacterial aerosols were measured as an index of NEW inactivation efficiency. The result shows that higher FAC concentration of NEW spray caused better inactivation efficiency. The Ka values under ACH 1.0 hr^?1 were 0.537 and 0.598 for E. coli of FAC 50 and 100 ppm spraying, respectively. The Ka values of FAC 100 ppm and 200 ppm spraying for B. subtilis were 0.063 and 0.085 under ACH 1.0 hr^?1, respectively. The results indicated that NEW spray is likely to be effective in inactivation of bacterial airborne contamination. Moreover, it is observed in the study that the increase of ventilation rate and the use of a larger orifice-size nozzle may facilitate the inactivation efficiency.

Implications: Bacterial aerosols have been implicated in deterioration of air quality and occupational health. Effective, safe, and economic control technology is highly demanded, especially for agricultural and food industries. In the study, NEW mist spraying performed effectively in controlling E. coli and B. subtilis modeling bioaerosols contamination. The NEW revealed its potential as an alternative airborne disinfectant worth being discovered for improving the environmental quality in the future.     

Sorption-Desorption of Atrazine and Diuron in Soils from Southern Brazil

The objective of this study was to investigate the behavior of sorption and desorption of the herbicides atrazine (6-chloro-N ²-ethyl-N ⁴-isopropyl-1,3,5-triazine-2,4-diamine) and diuron [3-(3,4-dichlorophenyl)-1,1-dimethyleurea] in soil samples from a typical lithosequence located in the municipality of Mamborê (PR), southern Brazil. Five concentrations of ¹⁴C-atrazine and ¹⁴C-diuron were used for both herbicides (0.48, 0.96, 1.92, 3.84, and 7.69 mg L^?1). Sorption of both herbicides correlated positively with the organic carbon and clay content of the soil samples. Sorption isotherms were well described by the Freundlich model. The slope values of the isotherm (N) ranged from 0.84 to 0.90 (atrazine) and from 0.75 to 0.79 (diuron) for the lithosequence samples. Sorption of diuron was high regardless of the soil texture or the concentration added. The desorption isotherms for atrazine and diuron showed good fit to the Freundlich equation (R ² ≥ 0,87). Atrazine slope values for the desorption isotherms were similar for the different concentrations and were much lower than those observed for the sorption isotherms. Significant hysteresis was observed in the herbicide desorption. When the two herbicides were compared, it was found that diuron (N = 0.06–0.22) presented more pronounced hysteresis than atrazine. The results showed that, quantitatively, a greater atrazine fraction applied to these soils remains available to be leached in the soil profile, as compared to diuron.     

Consultant Guide/1986

ABSTRACT

To obtain annual odor emission profiles from intensive swine operations, odor concentrations and emission rates were measured monthly from swine nursery, farrowing, and gestation rooms for a year. Large annual variations in odor concentrations and emissions were found in all the rooms and the impact of the seasonal factor (month) was significant (P < 0.05). Odor concentration was low in summer when ventilation rate was high but high in winter when ventilation rate was low, ranging from 362 (farrowing room in July) to 8934 (nursery room in December) olfactory unit (OU) m^?3. This indicates that the air quality regarding odor was significantly better in summer than that in winter. Odor emission rate did not show obvious seasonal pattern as odor concentration did, ranging from 2 (gestation room in November) to 90 (nursery room in April) OU m^?2 sec^?1; this explains why the odor complaints for swine barns have occurred all year round. The annual geometric mean odor concentration and emission rate of the nursery room was significantly higher than the other rooms (P < 0.05). In order to obtain the representative annual emission rate, measurements have to be taken at least monthly, and then the geometric mean of the monthly values will represent the annual emission rate. Incorporating odor control technologies in the nursery area will be the most efficient in reducing odor emission from the farm considering its emission rate was 2 to 3 times of the other areas. The swine grower-finisher area was the major odor source contributing 53% of odor emission of the farm and should also be targeted for odor control. Relatively positive correlations between odor concentration and both H₂S and CO₂ concentrations (R ² = 0.58) means that high level of these two gases might likely indicate high odor concentration in swine barns.

IMPLICATIONS The emissions of air pollutants including odors, greenhouse gases, and toxic gases have become a major environmental issue facing animal farms in the U.S.A. and Canada. To ensure the air quality in the vicinity of intensive livestock farms, air dispersion models have been used to determine setback distances between livestock facilities and neighboring residences based on certain air quality requirement on odor and gases. Due to the limited odor emission data available, none of the existing models can take account of seasonal variations of odor emissions, which may result in great uncertainties in setback distance calculations. Therefore, the obtained seasonal odor and gas emission rates by this study can be used by the government regulatory organizations and researchers in air dispersion modeling to get improved calculation of setback distances.     

The Silver Valley Lead Study:The Relationship between Childhood Blood Lead Levels and Environmental Exposure

This paper is directed to those persons concerned with the relationship between blood lead levels and environmental exposures to lead. Information presented in this paper represents one of the largest collections of epidemiologica! data relating blood lead levels to environmental exposures. The observed annual average ambient air lead concentration ranged from approximately 0.5 ng Pb/m³ to 23 ng Pb/m³, while lead in soil ranged from 50-24,600 ppm. Blood lead levels of children (ages 1-9 years) are related to a host of environmental variables via regression techniques. Blood lead levels were found to be most influenced by five variables. These variables are: ambient air lead, soil lead, age of the child, dustiness of the home, and occupational status of the parents. It is concluded, based upon the results of this study, that any environmental control strategy should address both the air and the soil. Soil levels in excess of 1000 ppm lead as well as air lead levels greater than 2 jug Pb/m³, 30 day average, were found to be unacceptable.     

Black carbon emissions in gasoline vehicle exhaust: A measurement and instrument comparison

A pilot study was conducted to evaluate the performance and agreement of several commercially available black carbon (BC) measurement instruments, when applied to the quantification of BC in light-duty vehicle (LDV) exhaust. Samples from six vehicles, three fuels, and three driving cycles were used. The pilot study included determinations of the method detection limit (MDL) and repeatability. With respect to the MDL, the real-time instruments outperformed the time-integrated instruments, with MDL = 0.12 mg/mi for the AE51 Aethalometer, and 0.15 mg/mi for the Micro Soot Sensor (MSS), versus 0.38 mg/mi for the IMPROVE_A thermal/optical method, and 0.35 mg/mi for the OT21_T Optical Transmissometer. The real-time instruments had repeatability values ranging from 30% to 35%, which are somewhat better than those of the time-integrated instruments (40–41%). These results suggest that, despite being less resource intensive, real-time methods can be equivalent or superior to time-integrated methods in terms of sensitivity and repeatability. BC mass data, from the photoacoustic and light attenuation instruments, were compared against same-test EC data, determined using the IMPROVE_A method. The MSS BC data was well correlated with EC, with R ² = 0.85 for the composite results and R² = 0.86 for the phase-by-phase (PBP) results. The correlation of BC, by the AE51, AE22, and OT21_T, with EC was moderate to weak. The weaker correlation was driven by the inclusion of US06 test data in the linear regression analysis. We hypothesize that test-cycle-dependent BC:EC ratios are due to the different physicochemical properties of particulate matter (PM) in US06 and Federal Test Procedure (FTP) tests. Correlation amongst the real-time MSS, PASS-1, AE51, and AE22 instruments was excellent (R² = 0.83–0.95), below 1 mg/mi levels. In the process of investigating these BC instruments, we learned that BC emissions at sub-1 mg/mi levels can be measured and are achievable by current-generation gasoline engines.

Implications: Most comparison studies of black carbon (BC) measurement methods were carried out in the ambient air. This study assesses the agreement among various BC measurement instrument in emissions from light-duty gasoline vehicles (LDGVs) on standard test cycles, and evaluates applicability of these methods under various fuel types, driving cycles, and engine combustion technologies. This research helps to fill in the knowledge gap of BC method standardization as stated in the U.S. Environmental Protection Agency (EPA) 2011 Report to Congress on Black Carbon, and these results demonstrate the feasibility of quantification of BC at the 1 mg/mi PM standard in California Low Emission Vehicle III regulations.     

Characterization of Activated Carbon Fiber Filters for Pressure Drop,Submicrometer Particulate Collection,and Mercury Capture

ABSTRACT

The use of activated carbon fiber (ACF) filters for the capture of particulate matter and elemental Hg is demonstrated. The pressure drop and particle collection efficiency characteristics of the ACF filters were established at two different face velocities and for two different aerosols: spherical NaCl and combustion-generated silica particles. The clean ACF filter specific resistance was 153 kg m^-2 sec^-1. The experimental specific resistance for cake filtration was 1.6 × 10⁶ sec^-1 and 2.4 × 10⁵ sec^-1 for 0.5- and 1.5-μm mass median diameter particles, respectively. The resistance factor R was approximately 2, similar to that for the high-efficiency particulate air filters. There was a discrepancy in the measured particle collection efficiencies and those predicted by theory. The use of the ACF filter for elemental Hg capture was illustrated, and the breakthrough characteristic was established. The capacity of the ACF filter for Hg capture was similar to other powdered activated carbons.     

Modeling of Carbon Monoxide Hot Spots

This paper describes the results of a measurement and modeling study of carbon monoxide (CO) concentrations In the proximity of intersections. Analysis for model performance of paired observed and predicted CO concentrations are presented. Two methodologies of pollutant prediction were used: the Intersection Midblock Model (IMM) and a statistical multiple linear regression. The results showed that both methods underpredicted frequently and dispensed results that were site specific. In addition, correlations of IMM predicted concentrations to observed concentrations were poor (typically r² values <0.25). Various explanations for this observation are proposed. The statistical approach exhibited an improved accuracy over that of IMM. However, some of the independent variables used might be difficult to obtain as a routine measurement, and use of a one or two independent parameter model yielded adjusted R² values comparable to the r² values observed with IMM. Based on these results, an Intersection model applicable under a wide range of conditions of traffic, meteorology, and geometry is not available. Research Is needed to develop one, since its use would often be called on in the development of air quality sections of Environmental Assessments or Environmental Impact Statements.     

A Comparative Analysis of Modeled and Monitored Ambient Hazardous Air Pollutants in Texas: A Novel Approach Using Concordance Correlation

Abstract

Often, in studies evaluating the health effects of hazardous air pollutants (HAPs), researchers rely on ambient air levels to estimate exposure. Two potential data sources are modeled estimates from the U.S. Environmental Protection Agency (EPA) Assessment System for Population Exposure Nationwide (ASPEN) and ambient air pollutant measurements from monitoring networks. The goal was to conduct comparisons of modeled and monitored estimates of HAP levels in the state of Texas using traditional approaches and a previously unexploited method, concordance correlation analysis, to better inform decisions regarding agreement. Census tract-level ASPEN estimates and monitoring data for all HAPs throughout Texas, available from the EPA Air Quality System, were obtained for 1990, 1996, and 1999. Monitoring sites were mapped to census tracts using U.S. Census data. Exclusions were applied to restrict the monitored data to measurements collected using a common sampling strategy with minimal missing values over time. Comparisons were made for 28 HAPs in 38 census tracts located primarily in urban areas throughout Texas. For each pollutant and by year of assessment, modeled and monitored air pollutant annual levels were compared using standard methods (i.e., ratios of model-to-monitor annual levels). Concordance correlation analysis was also used, which assesses linearity and agreement while providing a formal method of statistical inference. Forty-eight percent of the median model-to-monitor values fell between 0.5 and 2, whereas only 17% of concordance correlation coefficients were significant and greater than 0.5. On the basis of concordance correlation analysis, the findings indicate there is poorer agreement when compared with the previously applied ad hoc methods to assess comparability between modeled and monitored levels of ambient HAPs.     

Determination of three cresol isomers in sewage sludge by solid-liquid extraction with low temperature purification and gas chromatography-mass spectrometry

Abstract

Cresols are chemical contaminants derivative from phenol which can be found in sewage sludge. However, little attention has been given to monitoring these compounds in environmental matrices in the literature. Thus, the objective of this study was to develop a simple method based on solid-liquid extraction with low temperature purification for determining three cresol isomers in sludge. The quantification of these compounds was performed by gas chromatography coupled to mass spectrometry with a previous derivatization step. After a detailed study, the cresol recovery was higher than 91%, with relative standard deviation lower than 12% and a limit of quantification of 20?μg kg^?1. Linearity was achieved between 10 and 90?μg L^?1 (R² > 0.98) with the standard solutions prepared in matrix extracts due to the trouble caused by the matrix effect. The proposed method was applied with success for monitoring cresols in sewage sludge samples coming from six different wastewater treatment plants. All samples showed contamination by cresols, mainly p-cresol with values between 32.3 and 516.9?μg kg^?1. The majority of the analyzed samples showed a total sum of the isomers higher than the maximum residue limit established by Brazilian legislation (160?μg kg^?1).     

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial,California, USA

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM_2.5 and PM₁₀ (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R² for converted hourly averaged Dylos mass measurements versus a PM_2.5 BAM was 0.79 and that versus a PM₁₀ BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM_2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R² = 0.35–0.81).

Implications: The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.     

Modeled and observed fine particulate matter reductions from state attainment demonstrations

States rely upon photochemical models to predict the impacts of air quality attainment strategies, but the performance of those predictions is rarely evaluated retrospectively. State implementation plans (SIPs) developed to attain the 1997 U.S. standard for fine particulate matter (PM_2.5; denoting particles smaller than 2.5 microns in diameter) by 2009 provide the first opportunity to assess modeled predictions of PM_2.5 reductions at the state level. The SIPs were the first to rely upon a speciated modeled attainment test methodology recommended by the U.S. Environmental Protection Agency to predict PM_2.5 concentrations and attainment status. Of the 23 eastern U.S. regions considered here, all but one achieved the 15 μg/m³ standard by 2009, and the other achieved it the following year, with downward trends sustained in subsequent years. The attainment tests predicted 2009 PM_2.5 design values at individual monitors with a mean bias of 0.38 μg/m³ and mean error of 0.68 μg/m³, and were 95% accurate in predicting whether a monitor would achieve the standard. All of the errors were false alarms, in which the monitor observed attainment after a modeled prediction of an exceedance; in these cases, the states used weight-of-evidence determinations to argue that attainment was likely. Overall, PM_2.5 concentrations at monitors in the SIP regions declined by 2.6 μg/m³ from 2000–2004 to 2007–2009, compared with 1.6 μg/m³ in eastern U.S. regions originally designated as attainment. Air quality improvements tended to be largest at monitors that were initially the most polluted.

ImplicationsAs states prepare to develop plans for attaining a more stringent standard for fine particulate matter, this retrospective analysis documents substantial and sustained air quality improvements achieved under the previous standard. Significantly larger air quality improvements in regions initially designated nonattainment of the 1997 standard indicate that this status prompted heightened control efforts. The speciated modeled attainment test is found to be accurate and slightly conservative in predicting particulate concentrations for the cases considered here, providing confidence for its use in upcoming attainment plans.     

Quantification and Analysis of Airborne Bacterial Characteristics in a Nursing Care Institution

ABSTRACT

Indoor air quality has become a critical issue because people spend most of their time in the indoor environment. The factors that influence indoor air quality are very important to environmental sanitation and air quality improvement. This study focuses on monitoring air quality, colony counts, and bacteria species of the indoor air of a nursing care institution. The regular colony counts in two different wards range from 55 to 600 cfu m^?3. Regression analysis results indicate that the bacterial colony counts have close correlation with relative humidity or carbon dioxide (CO₂) but not with carbon monoxide (CO) or ozone (O₃). Real-time PCR was used to quantify the bacterial pathogens of nosocomial infection, including Acinetobacter baumannii, Citrobacter freundii, Escherichia coli, Klebsiella pneumoniae, and methicillin-sensitive Staphylococcus aureus. The most abundant bacteria species in the air of the nursing care institution is E. coli.

IMPLICATIONS Indoor temperature, humidity, ventilation, accumulation of biological pollutants, and potential infection problems will seriously affect the indoor environments. Studying these factors is important to indoor environmental sanitation and air quality improvements. Results of using real-time PCR to evaluate the bacterial pathogens of nosocomial infection for a nursing care institution in Taiwan reveal that the main bacteria species existing in the indoor air is E. coli.     

Assessment of boscalid and pyraclostrobin disappearance and behavior following application of effective microorganisms on apples

ABSTRACT

The aim of this study is to assess the disappearance of boscalid (IUPAC name: 2-chloro-N-[2-(4-chlorophenyl)phenyl]pyridine-3-carboxamide) and pyraclostrobin (IUPAC name: methyl N-[2-[[1-(4-chlorophenyl)pyrazol-3-yl]oxymethyl]phenyl]-N-methoxycarbamate) residues in apple fruit, and to verify whether an organic fertilizer enriched with strains of antagonistic microorganisms can reduce pesticide residue levels. Field trials were conducted in a commercial orchard on apples of the Gloster variety, during 21 days after the treatment with Bellis 38 WG and the subsequent application of Zumba Plant formulation containing Bacillus spp., Trichoderma spp. and Glomus spp. In control samples, the decrease rate of boscalid and pyraclostrobin residue levels followed an exponential function, described by formulae R_t = 0.2824e^?0.071t and R_t = 0.1176e^?0.060t, with the coefficient of determination of r² = 0.8692 and r² = 0.9268, respectively. These levels dropped to half (t_1/2) of their initial values after 9.8 and 11.5 days, respectively. The treatment with Zumba Plant resulted in a reduction in boscalid and pyraclostrobin residue levels by 52% and 41%, respectively. The results of this study are of importance for horticulture sciences and for producers of apples using plant protection products (PPPs).     

Distribution Equilibrium of Mercury (II) Chloride between Water and Air Applied to Flue Gas Scrubbing

ABSTRACT

In the literature, different values of the distribution coefficient K_H for HgCl₂ between water and air are present in a range that spans more than 3 orders of magnitude. In order to determine if a waste incineration scrubber solution could become saturated with regard to HgCl₂, an accurate experimental determination of the distribution constant of HgCl₂ at elevated temperatures is needed. In this work, the coefficient has been determined at four different temperatures between 10 and 50 °C. The Arrhenius expression obtained is 5.5 x 10⁵ x exp[-(8060 ± 2200)/7] with a corresponding enthalpy for the process HgCl₂(aq)<» HgCl₂(g) of 67 ± 20 kJ/mole. K_H at 293 K was found to be ~5 x 10^-7 atm M^-1, which is in almost perfect agreement with an earlier study. Applying the obtained K_H values to waste incineration scrubber conditions shows that no major saturation effect will occur.     

Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R²?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R²?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R²?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.

     

Microwave synthesis and antifungal evaluations of some chalcones and their derived diaryl-cyclohexenones

Microwave irradiation (MWI) of acetophenones and substituted benzaldehydes in water resulted in a “green-chemistry” procedure for the preparation of chalcones (1-14), through base catalyzed Claisen-Schmidt condensation reaction, in good yields. Further 3,5-diaryl-6-carbethoxy-2-cyclohexen-1-ones (1a-14a) were prepared through base catalyzed cyclocondensation of above chalcones with ethylacetoacetate using MWI as the energy source and silica as support. Out of fourteen cyclohexenones, ten (1a, 4a, 5a, 6a, 7a, 9a, 10a, 11a, 12a and 13a) are reported for the first time in literature. The synthesized compounds were characterized using various spectroscopic techniques, viz. (¹H NMR and IR) and screened for their antifungal activity in vitro against Sclerotium rolfsii and Rhizoctonia solani by poisoned food technique. The compounds tested were found to be active against R. solani whereas against S. rolfsii, moderate activity was observed, as evident from LC₅₀ values. The most potent compounds against R. solani were 1-(4-Fluoro-phenyl)-3-phenyl-propenone (13) and 1,3-Diphenyl-propenone (14) having LC₅₀ values of 2.36 and 2.49 mgL^{? 1} respectively (LC₅₀ of Hexaconazole = 1.12 mgL^{? 1}) and against S. rolfsii 3-(4-Fluoro-phenyl)-5-(3-nitro-phenyl)-6-carbethoxy-2-cyclohexen-1-one (12a) was most active having LC₅₀ value of 285 mgL^{? 1}compared to Hexaconazole (LC₅₀ = 1.27 mgL^{? 1}).