Tightening of air quality standards for populated urban areas has led to increasing attention to assessment of air quality management areas, where violation of air quality standards occurs, and development of control strategies to eliminate such violation of air quality standards. The Quetta urban area is very densely built and has heavy motorized traffic. The increase of emissions mainly from traffic and industry are responsible for the increase in atmospheric pollution levels during the last years. The dust examined in the current study was collected by both deposit gauge and Petri dish methods at various sites of Quetta Valley. Smoke particles were obtained by bladder method from the exhausts of various types of motor vehicles. The concentration of lead found in the smoke ranged from 1.5x10(-6) to 4.5x10(-6).

This study aims to examine the underlying mechanism of the relationship between perceived green human resource management (GHRM) and perceived employee green behavior (EGB). By drawing on attitude and social exchange theories, we examined green commitment (GC) as a mediator and green knowledge sharing (GKS) as a moderator of the GHRM–EGB relationship. The study employs partial least square structural equation modeling (PLS-SEM) to analyze 329 responses. Data were collected in two time lags. The empirical results confirmed that GC mediates the relationship between GHRM and EGB. However, the study results found that GKS moderated the indirect influence of GHRM on green behavior via GC. This research signifies the effect of GHRM, GKS, GC, and green behavior on organizations’ sustainability and environmental management. Despite the emerging literature on the significance of green practices in organizations for environmental management, no study has examined the moderating role of GKS on the indirect effect of GHRM on green behavior via mediating role of GC. This study offers valuable insight into environmental management in organizations through green practices and green behavior.

Nowadays, modeling and forecasting electricity spot prices are challenging due to their specific features, including multiple seasonalities, calendar effects, and extreme values (also known as jumps, spikes, or outliers). This study aims to provide a comprehensive analysis of electricity price forecasting by comparing several outlier filtering techniques followed by various modeling frameworks. To this end, extreme values are first treated with five different filtering techniques and are then replaced by four different outlier replacement approaches. Next, the spikes-free series is divided into deterministic and stochastic components. The deterministic component includes long-term trend, yearly and weekly seasonalities, and bank holidays and is estimated through parametric and nonparametric approaches. On the other hand, the stochastic component accounts for the short-run dynamics of the price time series and is modeled using different univariate and multivariate models. The one-day-ahead out-of-sample forecast results for the Italian Power Exchange (IPEX), obtained for a whole year, suggest that the outliers pre-filtering give a high accuracy gain. In addition, multivariate modeling for the stochastic component outperforms univariate models.INDEX TERMS Electricity prices, Forecasting, Extreme values treatment, IPEX, Parametric and nonparametric estimation.

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The catalytic decomposition of nitrous oxide (2N20 + 2N2 +02) has been studied on four series of Na-A zeolites exchanged with varying quantities of cobalt, copper, nickel and manganese. Decomposition obeys first-order kinetics without oxygen retention on all catalysts studied. All zeolites except those containing copper show linear Arrhenius plots. The rate-determining step in N,O decomposition is suggested to be electron transfer from the zeolite to adsorbed nitrous oxide. Activation energies are markedly higher for sieves containing an average of only one transition metal ion per unit cell compared with those where the transition metal ion content exceeds 1.5 per unit cell. This is also considered to reflect variation in the electronic properties of the catalysts with transition metal ion content. Transition metal ion exchanged zeolites are very much less active for N,O decomposition than bulk oxides or solid solutions but are of similar activity to transition metal ion exchanged X zeolites.