Climate Changes and Atmospheric Pollution

The objective of this chapter is to present the central concepts, parameters, and methods for the monitoring of climate changes, with a focus on air pollution, and the possible global and regional impacts of climate changes as well. There are plant species used as bioindicators that have a high sensitivity or ability to accumulate environmental pollutants. Another method that this chapter will present is the use of receiver models that employ both mathematical and statistical approaches to quantify the individual contribution of a given number of emission sources in the composition of a sample. The data presented in this chapter will provide reliable bases and methodologies for environmental control, supporting the adoption of more restrictive policies.

Simulation of Staple Crop Yields for Determination of Regional Impacts of Climate Change: A Case Study in Chonnam Province, Republic of Korea

This study sought to simulate regional variation in staple crop yields in Chonnam Province, Republic of Korea (ROK), in future environments under climate change based on the calibration of crop models in the Decision Support System for Agricultural Technology Transfer 4.6 package. We reproduced multiple-year yield data for paddy rice (2013–2018), barley (2000–2018), and soybean (2004–2018) grown in experimental fields at Naju, Chonnam Province, using the CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system developed using the crop models was then applied to simulate the regional impacts of climate change on the staple crops according to the Representative Concentration Pathway 4.5 and 8.5 scenarios. Simulated crop yields agreed with the corresponding measured crop yields, with root means square deviations of 0.31 ton ha−1 for paddy rice, 0.29 ton ha−1 for barley, and 0.27 ton ha−1 for soybean. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in the impact of climate change on staple crop yield. The CERES and CROPGRO models seem to reproduce the effects of climate change on region-wide staple crop production in a monsoonal climate system. Added advancements of the GCSM system could facilitate interpretations of future food resource insecurity and establish a sustainable adaption strategy.

Evidence of a Threshold Size for Norwegian Campsites and Its Dynamic Growth Process Implications—Does Gibrat’s Law Hold?

Although campsites are an important segment of the tourist sector, few applied articles have analyzed their growth path and tested Gibrat’s Law for firms within this industry. This knowledge can be of importance to the authorities when analyzing the regional impacts of growth in this sector. With government statistics from the last decade, we use a GMM framework to test the stricter version of Gibrat’s Law, which consist of three parts: the campsites’ growth trend, how they carry over success and failure, and how volatile their size is. The first and third part are rejected for Norwegian campsites, leading to a rejection of Gibrat’s Law. To see if firms of different sizes follow different dynamics, we split the sample in three parts. Here, we find evidence of a threshold size, as large campsites follow a fundamentally different dynamic than small and medium campsites. Specifically, large campsites gain no stability in revenue by further increases in size, whereas they carry over success/failure across years. The opposite is true for the rest of the sector. Gibrat’s Law is rejected on at least one count for each of the sub-samples. Lastly, we supplement the analysis with economy-wide and firm-specific variables to test further hypotheses.

The Problem

This chapter frames the problem of climate change. It opens with a brief overview of Earth’s energy balance and the greenhouse effect and then outlines the root causes of the problem along with key controls in the climate system that determine its responses. This is followed by an introduction of spatial variability and fluctuations through time in the expressions of climate change, which are key to understanding regional impacts. Such geographic and temporal variations do not invalidate the existence of the global average temperature increase, but merely cause fluctuations around the global average. Finally, the chapter shows that achieving the Paris Agreement’s 1.5°C warming limit will require the removal of 260–1030 billion tons of atmospheric carbon dioxide. The low-end estimate applies to best-case scenarios and the high-end estimate to scenarios where business-as-usual (as in the past two decades) emissions are allowed until 2030 or beyond.

SEIMR/R-S. General Epidemic Simulation Model Multi-Infected States - Multi Sociodemographic Segments - Multi-Region Mobility. Theory

SEIMR/R-S corresponds to a generalized mathematical model of pandemics that enhances traditional, aggregated simulation models when considering inter-regional impacts in a macro region (conurbed); SEIMR/R-S also considers the impact of modeling the population divided into sociodemographic segments based on age and economic stratum (it is possible to include other dimensions, for example: ethnics, gender, … ). SEIMR/R-S is the core of the SEIMR/R-S/OPT epidemic management optimization model that determines optimal policies (mitigation and confinement) considering the spatial distribution of the population, segmented sociodemographically and multiple type of vaccines. The formulation of SEIMR/R-S/OPT is presented by Velasquez-Bermudez (2021a) that includes the modeling of the vaccination process. SEIMR/R-S can be understood and used by any epidemiologist, and/or physician, working with SIR, SEIR or similar simulation models, and by professionals working on the issue of public policies for epidemic control. Following the theory presented in this document, ITCM (Instituto Tecnologico de Ciudad Madero, México) implemented the SEIMR/R-S epidemic model in a JAVA program (Velasquez-Bermudez et. al, 2021). This program may be used by the organizations that considers the SEIMR/R-S will be useful for management the COVID-19 pandemic, it is presented by VelasquezBermudez et al. (2021).