Rice straw can adsorb Cd(II) from wastewater, and modification of rice straw may improve its adsorption efficiency. The rice straw powder (Sp) from the direct pulverization of rice straw was used as the control, the rice straw ash (Sa), biochar (Sa), and modified rice straw (Ms) were prepared by ashing, pyrolysis and citric acid modification, respectively, and all of them were examined as adsorbents for Cd(II) in this study. Batch adsorption experiments were adopted to systematically compare the adsorption capacities of rice straw materials prepared with different modification methods for Cd(II) from aqueous solution under different levels of initial Cd(II) concentration (0–800 mg·L⁻¹), temperature (298, 308, and 318 K), contact time (0–1440 min), pH value (2–10), and ionic strength (0–0.6 mol·L⁻¹). The results indicated that the modification method affected the adsorption of Cd(II) by changing the specific surface area (SSA), Si content, surface morphology, and O-containing functional group of rice straw. Compared with Sp, Ms held more surface O–H, aliphatic and aromatic groups, while Sa had more phenolic, C–O (or C–O–C), and Si–O groups, and Sb held more C–O (or C–O–C) and Si–O groups; besides, Sa, Sb, and Ms had larger SSA than Sp. Adsorption capacity of the four adsorbents for Cd(II) increased and gradually became saturated with the increase in the initial Cd(II) concentration (0–800 mg·L⁻¹). The adsorption capacity of Cd(II) was significantly higher at 318 K than 298 K and 308 K, regardless of the adsorbent type. Sa had the largest SSA (192.38 m²·g⁻¹) and the largest adsorption capacity for Cd(II). When the initial Cd²⁺ concentration was at 800 mg·L⁻¹, the Cd(II) adsorption amount reached as high as 68.7 mg·g⁻¹ with Sa at 318 K. However, the SSA of Sp was only 1.83 m²·g⁻¹, and it had the least adsorption capacity for Cd(II). Only the adsorption of Cd(II) upon Sb at 298 K was spontaneous, and surprisingly, all other adsorptions were nonspontaneous. These adsorptions were all chemical, and were favorable, exothermic and order-increasing processes. The pseudo-second-order model showed a strong fit to the kinetics of Cd(II) adsorption by the four adsorbents. The adsorption capacities of Cd(II) by the adsorbents were less at low pH, and all were enhanced with the increase of initial pH value (2–10) in the solution. The inhibiting effect on Cd(II) adsorption due to the increase in ionic strength was greater with Sa, Sb, and Ms than that under Sp. The rice straw ash prepared by ashing unexpectedly had greater adsorption capacity for Cd(II) than the biochar and citric acid modified rice straw. The optimum condition for Cd(II) adsorption was established as the temperature of 318 K, initial Cd(II) concentration of 800 mg·L⁻¹, contact time of 240 min, and no Na(I) interference regardless of absorbent. In conclusion, rice straw ash shows the greatest potential of being applied to paddy fields for the remediation of Cd(II) pollution so as to reduce the risk of Cd(II) enrichment in rice grains and straws.

A standardized soil health scoring system would be a beneficial tool for accessing and interpreting the status of soil health in Florida. However, because of the climate variability and diverse soil types, a soil health soring function designed in one location may not be useful for other places. In this study, we focused on developing scoring functions for Florida soil using a soil health indicator database under different land management practices across Florida. A soil health scoring index for 11 soil health indicators was calculated for 398 soil samples using comprehensive assessment of soil health (CASH), linear or nonlinear scoring function, and additive and weighted‐additive methods. Based on communality and weights of total data sets (TDS), Mehlich‐3 phosphorus (M3P) contributed the most, whereas pH contributed the least to the indices. The nonlinear scoring function had higher scores than other scoring functions. Only mixed cropping systems had significant changes in soil health indicators before and after treatment. The principal component analysis (PCA) showed that OM and M3P had the highest loading values compared with other indicators. Therefore, OM and M3P need to be considered if minimum number of indicators is to be measured while examining soil health indices pertinent to Florida soil types and land management practices. Overall, the findings of this study provide a foundation for creating and enhancing soil health indices in Florida.

Woodchip bioreactor (WBR) is one of the green infrastructures in the agriculture system to reduce nitrate from agricultural drainage and stormwater. A lot of abiotic factors have been reported that affect nitrate removal lacking a comprehensive understanding. In this study, we studied eight important abiotic factors, including media age, hydraulic retention time (HRT), influent nitrate concentration (C_in), temperature, dissolved organic carbon (DOC), dissolved oxygen (DO), pH, and effective porosity (ρ_e) of WBR-filling materials. Based on a database that included 10,179 sets of data from 63 peer-reviewed articles, the nitrate removal rate (NRR) and nitrate removal efficiency (NRE) corresponding to the eight abiotic factors by different categories were comprehensively reported. According to this database, this study found the optimal range of abiotic factors for NRR and NRE in WBR were different. Regarding NRR, the optimal range of media age, HRT, C_in, temperature, effluent DOC, DO, pH, and ρ_e were in the first year, 0-5 h, 10-20 mg L^-1, 20-25 °C, 0-5 mg L^-1, 0-0.5 mg L^-1, 7-8, and 0.6-0.7, respectively. For NRE, the optimal range of media age, HRT, C_in, temperature, effluent DOC, DO, pH, and ρ_e were in the first year, 500-3000 h, 0-10 mg L^-1, 10-15 °C, >50 mg L^-1, 0-0.5 mg L^-1, 4-5, and 0.4-0.5, respectively. Moreover, the principal component analysis (PCA) indicated the field studies' principal components were different from laboratory studies. Furthermore, the structural equation modeling (SEM) also revealed the causal relationship of the eight abiotic factors on NRR and NRE is totally different. Lessons from this study can be incorporated into DNBR designs, especially improving nitrate removal rates by optimizing different abiotic factors. It also provides insights regarding the contributions of different abiotic factors for NRR and NRE independently and comprehensively.