Driving Factors and Scale Effects of Residents' Willingness to Pay for Environmental Protection under the Impact of COVID-19

In recent years, environmental degradation and the COVID-19 pandemic have seriously affected economic development and social stability. Addressing the impact of major public health events on residents' willingness to pay for environmental protection (WTPEP) and analyzing the drivers are necessary for improving human well-being and environmental sustainability. We designed a questionnaire to analyze the change in residents' WTPEP before and during COVID-19 and an established ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multiscale GWR to explore driver factors and scale effects of WTPEP based on the theory of environment Kuznets curve (EKC). The results show that (1) WTPEP is 0–20,000 yuan before COVID-19 and 0–50,000 yuan during COVID-19. Residents' WTPEP improved during COVID-19, which indicates that residents' demand for an ecological environment is increasing;(2) The shapes and inflection points of the relationships between income and WTPEP are spatially heterogeneous before and during COVID-19, but the northern WTPEP is larger than southern, which indicates that there is a spatial imbalance in WTPEP;(3) Environmental degradation, health, environmental quality, and education are WTPEP's significant macro-drivers, whereas income, age, and gender are significant micro-drivers. Those factors can help policymakers better understand which factors are more suitable for macro or micro environmental policy-making and what targeted measures could be taken to solve the contradiction between the growing ecological environment demand of residents and the spatial imbalance of WTPEP in the future.

Building health system resilience and pandemic preparedness using wastewater-based epidemiology from SARS-CoV-2 monitoring in Bengaluru, India.

The COVID-19 pandemic was a watershed event for wastewater-based epidemiology (WBE). It highlighted the inability of existing disease surveillance systems to provide sufficient forewarning to governments on the existing stage and scale of disease spread and underscored the need for an effective early warning signaling system. Recognizing the potentiality of environmental surveillance (ES), in May 2021, COVIDActionCollaborative launched the Precision Health platform. The idea was to leverage ES for equitable mapping of the disease spread in Bengaluru, India and provide early information regarding any inflection in the epidemiological curve of COVID-19. By sampling both networked and non-networked sewage systems in the city, the platform used ES for both equitable and comprehensive surveillance of the population to derive precise information on the existing stage of disease maturity across communities and estimate the scale of the approaching threat. This was in contrast to clinical surveillance, which during the peak of the COVID-19 pandemic in Bengaluru excluded a significant proportion of poor and vulnerable communities from its ambit of representation. The article presents the findings of a sense-making tool which the platform developed for interpreting emerging signals from wastewater data to map disease progression and identifying the inflection points in the epidemiological curve. Thus, the platform accurately generated early warning signals on disease escalation and disseminated it to the government and the general public. This information enabled concerned audiences to implement preventive measures in advance and effectively plan their next steps for improved disease management.

Analysis of Growth Characteristics of Kimchi Cabbage Using Drone-Based Cabbage Surface Model Image

Cultivation soil is the basis for cabbage growth, and it is important to assess not only to provide information on how it affects the growth of vegetable crops but also for cultivation management. Until now, field cabbage surveys have measured size and growth variations in the field, and this method requires a lot of time and effort. Drones and sensors provide opportunities to accurately capture and utilize cabbage growth and variation data. This study aims to determine the growth stages based on drone remote estimation of the cabbage height and evaluate the impact of the soil texture on cabbage height. Time series variation according to the growth of Kimchi cabbage exhibits an S-shaped sigmoid curve. The logistic model of the growth curve indicates the height and growth variation of Kimchi cabbage, and the growth rate and growth acceleration formula of Kimchi cabbage can thus be derived. The curvature of the growth parameter can be used to identify variations in Kimchi cabbage height and its stages of growth. The main research results are as follows. (1) According to the growth curve, Kimchi cabbage growth can be divided into four stages: initial slow growth stage (seedling), growth acceleration stage (transplant and cupping), heading through slow growth, and final maturity. The three boundary points of the Kimchi cabbage growth curve are 0.2113 Gmax, 0.5 Gmax, and 0.7887 Gmax, where Gmax is the maximum height of Kimchi cabbage. The growth rate of cabbage reaches its peak at 0.5 Gmax. The growth acceleration of cabbage forms inflection points at 0.2113 Gmax and 0.7887 Gmax, and shows a variation characteristic. (2) The produced logistic growth model expresses the variation in the cabbage surface model value for each date of cabbage observation under each soil texture condition, with a high degree of accuracy. The accuracy evaluation showed that R2 was at least 0.89, and the normalized root-mean-square error (nRMSE) was 0.09 for clay loam, 0.06 for loam, and 0.07 for sandy loam, indicating a very strong regression relationship. It can be concluded that the logistic model is an important model for the phase division of cabbage growth and height variation based on cabbage growth parameters. The results obtained in this study provide a new method for understanding the characteristics and mechanisms of the growth phase transition of cabbage, and this study will be useful in the future to extract various types of information using drones and sensors from field vegetable crops.

Prediction of numbers of the accumulative confirmed patients (NACP) and the plateau phase of 2019-nCoV in China.

In the present study, I propose a novel fitting method to describe the outbreak of 2019-nCoV in China. The fitted data were selected carefully from the non-Hubei part and Hubei Province of China respectively. For the non-Hubei part, the time period of data collection corresponds from the beginning of the policy of isolation to present day. But for Hubei Province, the subjects of Wuhan City and Hubei Province were included from the time of admission to the Huoshenshan Hospital to present day in order to ensure that all or the majority of the confirmed and suspected patients were collected for diagnosis and treatment. The employed basic functions for fitting are the hyperbolic tangent functions tanh ( . ) since in these cases the 2019-nCoV is just an epidemic. Subsequently, the 2019-nCoV will initially expand rapidly and tend to disappear. Therefore, the numbers of the accumulative confirmed patients in different cities, provinces and geographical regions will initially increase rapidly and subsequently stabilize to a plateau phase. The selection of the basic functions for fitting is crucial. In the present study, I found that the hyperbolic tangent functions tanh ( . ) could satisfy the aforementioned properties. By this novel method, I can obtain two significant results. They base on the conditions that the rigorous isolation policy is executed continually. Initially, I can predict the numbers very accurately of the cumulative confirmed patients in different cities, provinces and parts in China, notably, in Wuhan City with the smallest relative error estimated to 0.021 % , in Hubei Province with the smallest relative error estimated to 0.012 % and in the non-Hubei part of China with the smallest relative error of -  0.195% in the short-term period of infection. In addition, perhaps I can predict the times when the plateau phases will occur respectively in different regions in the long-term period of infection. Generally for the non-Hubei part of China, the plateau phase of the outbreak of the 2019-nCoV will be expected this March or at the end of this February. In the non-Hubei region of China it is expected that the epidemic will cease on the 30th of March 2020 and following this date no new confirmed patient will be expected. The predictions of the time of Inflection Points and maximum NACP for some important regions may be also obtained. A specific plan for the prevention measures of the 2019-nCoV outbreak must be implemented. This will involve the present returning to work and resuming production in China. Based on the presented results, I suggest that the rigorous isolation policy by the government should be executed regularly during daily life and work duties. Moreover, as many as possible the confirmed and suspected cases should be collected to diagnose or treat.