Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
This study focuses on the chemical composition of cloud water (CW) and rainwater (RW) collected at Sinhagad, a high-altitude station (1450 m AMSL) located in the western region of India. The samples were collected during the monsoon over two years (2016-2017). The chemical analysis suggests that the concentration of total ionic constituents was three times higher in CW than in RW, except for NH (1.0) and HCO (0.6). Compared to RW, high concentrations of SO and NO were observed in CW. The weighted average RW pH (6.5 ± 0.3) was slightly more alkaline than CW pH (6.1 ± 0.5). This can be attributed to the high concentrations of neutralizing ions such as nss-Ca, nss-Mg, K, and NH, indicating the greater extent of wet scavenging during rainfall. These ions counteract the acidity generated by SO and NO. A high correlation between Ca, Na, K, NO, and SO makes it difficult to estimate the contribution of SO from different sources. Anthropogenic sulfur emissions and soil dust significantly influence the ionic composition of clouds and rain. Positive matrix factorization (PMF) was used to identify the contribution of different sources to the samples. In the CW, the extracted factors were cooking and vehicles, aging sea salt, agriculture, and dust. In RW, the factors were industries, cooking and vehicles, agriculture and dust, and aging sea salt. The findings of this study have significant implications for the monsoon build-up, ecosystems, agriculture, and climate change.
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Source |
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http://dx.doi.org/10.1007/s11356-024-34793-7 | DOI Listing |
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