Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility

The hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM2.5) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM2.5 concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, $$f\left( {RH} \right)$$ f RH , and the hygroscopic mass growth, $$GM_{VIS}$$ G M VIS , which were applied to PM2.5 field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM2.5 concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM2.5. The modeled $$f\left( {RH} \right)$$ f RH agreed well with the observed $$f\left( {RH} \right)$$ f RH in the RH range of the haze and mist. Finally, the RH-adjusted PM2.5 concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.

How Much Do Farmers Expect to Implement for Traceability? Evidence From a Double-Bound Choices Experiment of Vietnamese Shrimp Aquaculture

Traceability is considered the most important requirement for shrimp products exported to global markets. However, implementing traceability in shrimp-exporting countries is challenging because of limited production at the local supply chain and lack of financial welfare awareness. This study aims to investigate the expected farm-gate price for traceability implementation using a double-bound dichotomous choice experiment. The censored regression model is used to estimate the factors influencing the anticipated farm-gate price of shrimp farmers. The survey was conducted in Ca Mau Province, Vietnam, by interviewing 71 Penaeus monodon Fabricius, 1798, and 43 Penaeus vannamei Boone, 1931, farmers. To implement traceability, P. monodon farmers estimated the farm-gate price at 10.17 USD.kg-1 , while P. vannamei farmers expected 6.18 USD.kg-1 . Application of international quality assurance certifications, willingness to implement traceability, land used, culture methods, shrimp species, current farm-gate price, and variable costs affected the expected farm-gate price. The attractive anticipated farm-gate price compensated for the negative influence of applying international quality assurance certifications, indicating that the farmers were willing to implement traceability. This suggests that the application of certifications increased the ability to implement traceability in the shrimp supply chain. The attractive farm-gate price for certified shrimp products would enhance their willingness to implement the traceability of shrimp products.

A three-stage model of the volatility-volume relation in the Junk bond market during the 2007-2008 financial crisis

Purpose The purpose of this paper is to examine the joint dynamics of volatility–volume relation in the high-yield (junk) corporate bond market during the 2007–2008 financial crisis. Design/methodology/approach The author proposes a new empirical model of three-stage equations to better estimate the volume–volatility relation that helps in alleviating three econometrical problems. In Stage 1, the author estimates the fitted values of trading volume using a censored regression model, to alleviate the truncation problems of using Transaction Reporting and Compliance Engine data. In Stage 2, the author calculates the fitted values of bond return volatility using asymmetric Sign-GARCH model, to control for the asymmetric volatility in return series. In Stage 3, the author uses the fitted values of trading volume from the censored regression model (Stage 1) and the fitted values of return volatility from the GARCH model (Stage 2), to better alleviate the endogeneity problems between both variables. Findings The central finding is that conclusions about the statistical significance and the direction of the volume–volatility relationship in the junk bond market are dependent on the econometric methodology used. Originality/value From a practitioner perspective, it is important for professional traders holding positions in fixed income securities in their trading accounts to be aware of their asymmetric time-varying volume–volatility shifting trends. Such knowledge helps traders diversify their positions and manage their portfolios more appropriately.

Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts

Statistical postprocessing models can be used to correct bias and dispersion errors in raw precipitation forecasts from numerical weather prediction models. In this study, we conducted experiments to investigate four factors that influence the performance of regression-based postprocessing models with normalization transformations for short-term precipitation forecasts. The factors are 1) normalization transformations, 2) incorporation of ensemble spread as a predictor in the model, 3) objective function for parameter inference, and 4) two postprocessing schemes, including distributional regression and joint probability models. The experiments on the first three factors are based on variants of a censored regression model with conditional heteroscedasticity (CRCH). For the fourth factor, we compared CRCH as an example of the distributional regression with a joint probability model. The results show that the CRCH with normal quantile transformation (NQT) or power transformation performs better than the CRCH with log–sinh transformation for most of the subbasins in Huai River basin with a subhumid climate. The incorporation of ensemble spread as a predictor in CRCH models can improve forecast skill in our research region at short lead times. The influence of different objective functions (minimum continuous ranked probability score or maximum likelihood) on postprocessed results is limited to a few relatively dry subbasins in the research region. Both the distributional regression and the joint probability models have their advantages, and they are both able to achieve reliable and skillful forecasts.