Effects of heating rate and temperature on product distribution of poly-lactic acid and poly-3-hydroxybutyrate-co-3-hydroxyhexanoate

In this study, poly-lactic acid (PLA) and poly-3-hydroxybutyrate-co-3-hydroxyhexanoate (PHBH) were pyrolyzed at various temperatures (300, 350, 400, 500, 600, and 700 °C) and heating rates (5, 10, 20, 30, and 40 °C min⁻¹) using a pyrolysis–gas chromatograph/mass spectrometer (Py–GC/MS). The results revealed that the main pyrolysis products of PLA were acetaldehyde, lactide (including meso-lactide and d-, l-lactide), and oligomers. Crotonic acid and its oligomers accounted for most of the PHBH pyrolyzates. The pyrolysis temperature significantly correlated with the product distribution, but the heating rate had a small effect on the product distribution. Lactide and crotonic acid were two kinds of high-value chemicals, and their highest yields were obtained at 400 and 600 °C with 29.7 and 72.6 area %, respectively. Secondary reactions could not be neglected at 700 °C, and acetaldehyde and crotonic acid decreased to 65.0 and 69.6 area %, respectively. These results imply that pyrolyzate selectivity can be controlled by temperature management during pyrolysis.

     

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.