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It is recognised that environmental air pollution is one of the global problems and is a common problem for both developing as well as developed countries. In the era of globalisation, it is the most important global environmental issue. In general, urban air quality is becoming vulnerable especially in the developing countries due to adopting various developmental schemes. Air pollution problem in Kolkata, capital city of West Bengal, is under serious for a long day. As per guidelines of World Health Organization, for residential areas, air pollution level in Kolkata is considerably higher than the standard enumerated. There are several types of air pollutants which are continuously exposing the air of Kolkata. West Bengal pollution control board (WBPCB) has been monitoring ambient air quality (AAQ) for the parameters viz. suspected particulate matters (SPM), respiratory particulate matters (RPM), sulphur dioxide (SO₂), nitrogen dioxide (NO₂) and lead (Pb) in Kolkata throughout the years. Present study has been designed to determine the vertical floor-wise air quality status of the city of Kolkata and the seasonal variation of the pollutants over the consecutive years from 2011 to 2017. It is demonstrated that the air pollution is the highest in the winter due to dry weather, second is festive season followed by winter due to heavy movement of vehicles and pedestrians for festival shoppings as well as pandel hoppings and then next is summer. But coming to the point of rainy season, this is the lowest due to wetted air or wind of monsoon. This chapter attempts to understand the long-run trend of air pollution as the periodical average value suggests.

Purpose: This paper aims to review the effect of the 2003 SARS epidemic on tourism and to analyze the learning outcomes from this crisis critically. The study further proposes practical and strategic tips for airlines, hoteliers and destination marketers in the top Asian tourism market such as India, China, Singapore and Thailand if a similar crisis affects the region.

Study Design/Methodology/Approach: The study followed the qualitative approach for deriving the findings. The research reviewed past literature by using the Prisma method, which is an evidence-based minimum set of items, for reporting in systematic reviews and meta-analyses. The research summarized the previous literature on the SARS outbreak in 2003 and reviewed various practices for tourism industry sustainability.

Finding: The study provides the information of how the airlines, hoteliers and destination marketers tackled the SARS epidemic and proposes the integrated strategy for managing such crisis in future.

Originality/Value: This paper performed the systematic literature review of all the relevant researches related to the 2003 SARS outbreak and its effect on the tourism industry.

Implications: This paper suggests the strategies and practices based on past reviews and learnings from experiences which may be useful for the tourism stakeholders to tackle and minimize the effect of such crisis in future.

Soon after beginning operations, the Federal Reserve established a nationwide network for collecting information about the economy. In 1919, the Fed began tabulating data by about retail sales, which it viewed as a fundamental measure of consumption. From 1920 until 1929, the Federal Reserve published data about retail sales each month by Federal Reserve district, but ceased to do so after 1929. It continued to compile monthly data on retail sales by reserve district, but this data remained in house. We collected these in-house reports from the archives of the Board of Governors and constructed a consistent series on retail trade at the district level. The new series enhances our understanding of economic trends during the Roaring ‘20s and Great Depression.

Product success is contingent on forecasting when a product is needed and how it should be offered. Forecasting accuracy is contingent on the correct forecasting technique. Using supermarket data across two product categories, this chapter shows that using a bevy of forecasting methods improves forecasting accuracy. Accuracy is measured by the mean absolute percentage error. The optimal methods for one consumer goods product may be different than for another. The best model varied from sophisticated, most such as autoregressive integrated moving average (ARIMA) and Holt–Winters to a random walk model. Forecasters must be proficient in multiple statistical techniques since the best technique varies within a categories, variety, and product size.

This chapter enquires into the contribution of two British writers, Herbert Somerton Foxwell and Henry Riverdale Grenfell, who elaborated upon the hints provided by Jevons towards a description of long waves in the oscillations of prices. Writing two decades after Jevons, they witnessed the era of high prices turning into the great depression of the last quarter of the nineteenth century, the causes of which they saw in the end of bimetallism. Not only did they take up Jevons’s specific explanation of the long fluctuations, but they also based their discussion upon graphical representation of data and incorporated in their treatment a specific trait (the superposition principle) of the ‘waves’ metaphor emphasized by the Manchester statisticians in the 1850s and 1860s. Their contribution is also interesting for their understanding of crises versus depressions at the time of the emergence of the interpretation of oscillations as a cycle, which they have only partially grasped – as distinct from the approach of later long wave theorists.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

The seasonality in the behavior of travelers is something that goes back to the origin of the trips themselves. This seasonality is due to multiple factors, some easy to counteract and others difficult to solve. But, regardless of the causes, it is a phenomenon that generates significant negative impacts on society and the environment in which the phenomenon of tourist seasonality occurs. All tourist destinations have seasonality, but in some cases, it is very high and in others it has a minimal incidence. The objective of this chapter is to ponder the impacts and consequences of seasonality in regions with a strong tourism development, allowing to put into context the aspects of society impacted by this phenomenon and the positive implications that the reduction of seasonality would have. For this, an analysis of a theoretical model with two regions in opposite situations is carried out, raising the possible effects of a high seasonality. The cases of the Balearic Islands and the Canary Islands are also reviewed, as real examples of the regional typologies taken into consideration in the theoretical model. This seeks to ponder the problems attributable to seasonality. As a final reflection, the enormous typology of negative impacts generated and the need to continue analyzing the seasonality and its impacts are emphasized.

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict retail sales exhibiting these patterns. Due to economic instability, recent retail sales time-series data show a higher degree of variability and nonlinearity, which makes the ARIMA model less accurate. This chapter demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) in forecasting aggregate retail sales. The hybrid forecasting method of integrating EMD and neural network (EMD-NN) models was applied to two real data sets from two different time periods. The one-period ahead forecasts for both time periods show that EMD-NN outperforms the classical NN model and seasonal ARIMA. In addition, the findings also indicate that EMD-NN can significantly improve forecasting performance during the periods in which macroeconomic conditions are more volatile.