Pollution characteristics and human health risk assessments of toxic metals and particle pollutants via soil and air using geoinformation in urbanized city of Pakistan.

Toxic metals and particle pollutants in urbanized cities have significantly increased over the past few decades mainly due to rapid urbanization and unplanned infrastructure. This research aimed at estimating the concentration of toxic metals and particle pollutants and the associated risks to public health across different land-use settings including commercial area (CA), urban area (UA), residential area (RA), and industrial area (IA). A total of 47 samples for both soil and air were collected from different land-use settings of Faisalabad city in Pakistan. Mean concentrations of toxic metals such as Mn, Zn, Pb, Ni, Cr, Co, and Cd in all land-use settings were 92.68, 4.06, 1.34, 0.16, 0.07, 0.03, and 0.02 mg kg-1, respectively. Mean values of PM10, PM2.5, and Mn in all land-use settings were found 5.14, 1.34, and 1.9 times higher than the World Health Organization (WHO) guidelines. Mn was found as the most hazardous metal in terms of pollution load index (PLI) and contamination factor (CF) in the studied area. Health risk analysis for particle pollutants using air quality index (AQI) and geoinformation was found in the range between good to very critical for all the land-use settings. The hazard quotient (HQ) and hazard index (HI) were higher for children in comparison to adults, suggesting that children may be susceptible to potentially higher health risks. However, the cancer risk (CR) value for Pb ingestion (1.21 × 10-6) in children was lower than the permissible limit (1 × 10-4 to 1 × 10-6). Nonetheless, for Cr inhalation, CR value (1.09 × 10-8) was close to tolerable limits. Our findings can be of valuable assistance toward advancing our understanding of soil and air pollutions concerning public health in different land-use settings of the urbanized cities of Pakistan.

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality.

BACKGROUND: The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS. RESULTS: The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with RNDSI 2 = 0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds. CONCLUSION: This study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies.