{"id":30711,"date":"2026-06-01T12:12:53","date_gmt":"2026-06-01T04:12:53","guid":{"rendered":"https:\/\/shchimay.com\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/"},"modified":"2026-06-01T12:12:53","modified_gmt":"2026-06-01T04:12:53","slug":"3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions","status":"publish","type":"post","link":"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/","title":{"rendered":"3 Critical Challenges in IoT Water Quality Monitoring (And Proven Solutions)"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_50 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#3_Critical_Challenges_in_IoT_Water_Quality_Monitoring_And_Proven_Solutions\" title=\"3 Critical Challenges in IoT Water Quality Monitoring (And Proven Solutions)\">3 Critical Challenges in IoT Water Quality Monitoring (And Proven Solutions)<\/a><ul class='ez-toc-list-level-2'><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Challenge_1_Sensor_Accuracy_and_Calibration_Drift\" title=\"Challenge 1: Sensor Accuracy and Calibration Drift\">Challenge 1: Sensor Accuracy and Calibration Drift<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#The_Problem\" title=\"The Problem\">The Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#The_Impact_on_IoT_Systems\" title=\"The Impact on IoT Systems\">The Impact on IoT Systems<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Proven_Solutions\" title=\"Proven Solutions\">Proven Solutions<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Challenge_2_Data_Connectivity_and_Transmission\" title=\"Challenge 2: Data Connectivity and Transmission\">Challenge 2: Data Connectivity and Transmission<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#The_Problem-2\" title=\"The Problem\">The Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#The_Impact_on_IoT_Systems-2\" title=\"The Impact on IoT Systems\">The Impact on IoT Systems<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Proven_Solutions-2\" title=\"Proven Solutions\">Proven Solutions<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Challenge_3_Data_Integration_and_Interpretation\" title=\"Challenge 3: Data Integration and Interpretation\">Challenge 3: Data Integration and Interpretation<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#The_Problem-3\" title=\"The Problem\">The Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#The_Impact_on_IoT_Systems-3\" title=\"The Impact on IoT Systems\">The Impact on IoT Systems<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Proven_Solutions-3\" title=\"Proven Solutions\">Proven Solutions<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Implementation_Roadmap\" title=\"Implementation Roadmap\">Implementation Roadmap<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Phase_1_Foundation_Months_1-3\" title=\"Phase 1: Foundation (Months 1-3)\">Phase 1: Foundation (Months 1-3)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Phase_2_Intelligence_Months_4-6\" title=\"Phase 2: Intelligence (Months 4-6)\">Phase 2: Intelligence (Months 4-6)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Phase_3_Optimization_Months_7-12\" title=\"Phase 3: Optimization (Months 7-12)\">Phase 3: Optimization (Months 7-12)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/shchimay.com\/es\/3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\/#Conclusion\" title=\"Conclusion\">Conclusion<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1 id=\"3-critical-challenges-in-iot-water-quality-monitoring-and-proven-solutions\"><span class=\"ez-toc-section\" id=\"3_Critical_Challenges_in_IoT_Water_Quality_Monitoring_And_Proven_Solutions\"><\/span>3 Critical Challenges in IoT Water Quality Monitoring (And Proven Solutions)<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<p><strong>Key Takeaways:<\/strong><br \/>\n&#8211; <strong>68%<\/strong> of IoT water monitoring projects face significant data quality issues<br \/>\n&#8211; Sensor calibration drift causes <strong>$45,000<\/strong> average annual cost in false readings<br \/>\n&#8211; Network connectivity problems affect <strong>42%<\/strong> of remote monitoring deployments<br \/>\n&#8211; Solutions exist for every major IoT water monitoring challenge<\/p>\n<p>The Internet of Things (IoT) promises transformative capabilities for water quality monitoring\u2014continuous data, remote access, and automated alerts. Yet many organizations struggle to realize these benefits. Industry surveys reveal that <strong>only 32%<\/strong> of IoT water monitoring projects achieve their intended outcomes.<\/p>\n<p>Understanding the critical challenges\u2014and proven solutions\u2014is essential for successful deployment.<\/p>\n<h2 id=\"challenge-1-sensor-accuracy-and-calibration-drift\"><span class=\"ez-toc-section\" id=\"Challenge_1_Sensor_Accuracy_and_Calibration_Drift\"><\/span>Challenge 1: Sensor Accuracy and Calibration Drift<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"the-problem\"><span class=\"ez-toc-section\" id=\"The_Problem\"><\/span>The Problem<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Inline water quality sensors drift over time. pH electrodes accumulate reference junction contamination, conductivity sensors suffer from electrode surface changes, and dissolved oxygen membranes degrade. This drift introduces measurement errors that compound over weeks or months.<\/p>\n<p>According to <strong>Water Research Foundation 2025<\/strong>, calibration drift costs water utilities an average of <strong>$45,000<\/strong> annually through:<br \/>\n&#8211; False compliance alerts triggering unnecessary investigations<br \/>\n&#8211; Missed contamination events due to sensor inaccuracy<br \/>\n&#8211; Excessive calibration labor<br \/>\n&#8211; Premature sensor replacement<\/p>\n<h3 id=\"the-impact-on-iot-systems\"><span class=\"ez-toc-section\" id=\"The_Impact_on_IoT_Systems\"><\/span>The Impact on IoT Systems<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>When sensors feed unreliable data to IoT platforms:<br \/>\n&#8211; AI anomaly detection generates excessive false positives<br \/>\n&#8211; Machine learning models produce inaccurate predictions<br \/>\n&#8211; Automated responses trigger inappropriate actions<br \/>\n&#8211; Operators lose trust in monitoring systems<\/p>\n<h3 id=\"proven-solutions\"><span class=\"ez-toc-section\" id=\"Proven_Solutions\"><\/span>Proven Solutions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Solution 1.1: Automated Calibration Verification<\/strong><\/p>\n<p>Deploy redundant sensors and implement cross-validation algorithms:<br \/>\n&#8211; Two sensors measure the same parameter simultaneously<br \/>\n&#8211; Algorithm detects when readings diverge beyond tolerance<br \/>\n&#8211; System alerts operators to potential drift before measurements become unreliable<\/p>\n<p>Modern inline pH sensors with <strong>built-in impedance monitoring<\/strong> can detect electrode degradation <strong>2-3 weeks<\/strong> before measurement accuracy is compromised.<\/p>\n<p><strong>Solution 1.2: Self-Cleaning Sensor Technology<\/strong><\/p>\n<p>Fouling causes measurement drift in challenging applications. Advanced sensors incorporate:<br \/>\n&#8211; <strong>Ultrasonic cleaning<\/strong> systems that vibrate sensor surfaces at 40 kHz<br \/>\n&#8211; <strong>Air sparging<\/strong> to prevent biofilm formation<br \/>\n&#8211; <strong>Automatic wiper mechanisms<\/strong> for turbidity sensors<br \/>\n&#8211; <strong>Chemical injection<\/strong> for cleaning calibration zones<\/p>\n<p>These systems extend calibration intervals from <strong>2-4 weeks<\/strong> to <strong>8-12 weeks<\/strong>, reducing maintenance labor and drift-related errors.<\/p>\n<p><strong>Solution 1.3: Virtual Sensor Redundancy<\/strong><\/p>\n<p>Machine learning creates virtual sensors that validate physical sensor readings:<br \/>\n&#8211; AI models predict expected values based on correlated parameters<br \/>\n&#8211; Physical sensor readings are compared against predictions<br \/>\n&#8211; Divergence triggers calibration verification alerts<\/p>\n<p>For example, conductivity can be predicted from pH, temperature, and ionic strength measurements. A drift in the physical conductivity sensor will show divergence from the virtual conductivity prediction.<\/p>\n<h2 id=\"challenge-2-data-connectivity-and-transmission\"><span class=\"ez-toc-section\" id=\"Challenge_2_Data_Connectivity_and_Transmission\"><\/span>Challenge 2: Data Connectivity and Transmission<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"the-problem_1\"><span class=\"ez-toc-section\" id=\"The_Problem-2\"><\/span>The Problem<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Water treatment facilities often span large geographic areas with challenging environments. Remote monitoring points may lack reliable network connectivity, causing data gaps that compromise system effectiveness.<\/p>\n<p><strong>IEEE IoT Journal<\/strong> research found that <strong>42%<\/strong> of remote water monitoring deployments experience significant connectivity issues, with average data loss of <strong>8.7%<\/strong> during normal operations and <strong>34%<\/strong> during severe weather events.<\/p>\n<h3 id=\"the-impact-on-iot-systems_1\"><span class=\"ez-toc-section\" id=\"The_Impact_on_IoT_Systems-2\"><\/span>The Impact on IoT Systems<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Connectivity problems manifest as:<br \/>\n&#8211; <strong>Gaps in data records<\/strong> that invalidate trend analysis<br \/>\n&#8211; <strong>Delayed alerts<\/strong> that miss time-critical events<br \/>\n&#8211; <strong>Buffer overflow<\/strong> when connectivity returns after outage<br \/>\n&#8211; <strong>Battery drain<\/strong> from repeated reconnection attempts<\/p>\n<h3 id=\"proven-solutions_1\"><span class=\"ez-toc-section\" id=\"Proven_Solutions-2\"><\/span>Proven Solutions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Solution 2.1: Edge Computing Architecture<\/strong><\/p>\n<p>Deploy intelligent edge devices that:<br \/>\n&#8211; Process data locally during connectivity outages<br \/>\n&#8211; Store data in local memory until transmission resumes<br \/>\n&#8211; Analyze data streams for immediate alerts without cloud connectivity<br \/>\n&#8211; Sync with central systems when connectivity is available<\/p>\n<p>Edge computing reduces data loss during outages to <strong>&lt;1%<\/strong> while maintaining real-time alerting capability.<\/p>\n<p><strong>Solution 2.2: Multi-Network Redundancy<\/strong><\/p>\n<p>Modern IoT monitoring systems utilize multiple communication pathways:<br \/>\n&#8211; <strong>Cellular LTE-M\/NB-IoT<\/strong> as primary connection<br \/>\n&#8211; <strong>LoRaWAN<\/strong> for long-range, low-power remote sites<br \/>\n&#8211; <strong>Satellite<\/strong> for extremely remote locations<br \/>\n&#8211; <strong>Wi-Fi<\/strong> for infrastructure-connected locations<br \/>\n&#8211; <strong>Serial\/Modbus<\/strong> for site-wide backhaul<\/p>\n<p>Devices automatically switch to available networks, ensuring <strong>99.5%<\/strong> connectivity uptime.<\/p>\n<p><strong>Solution 2.3: Store-and-Forward Protocols<\/strong><\/p>\n<p>Implement communication protocols designed for intermittent connectivity:<br \/>\n&#8211; Data packets include timestamps and sequence numbers<br \/>\n&#8211; Buffer storage capacity for <strong>7+ days<\/strong> of readings<br \/>\n&#8211; Intelligent compression to maximize storage efficiency<br \/>\n&#8211; Automatic retry with exponential backoff<\/p>\n<h2 id=\"challenge-3-data-integration-and-interpretation\"><span class=\"ez-toc-section\" id=\"Challenge_3_Data_Integration_and_Interpretation\"><\/span>Challenge 3: Data Integration and Interpretation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"the-problem_2\"><span class=\"ez-toc-section\" id=\"The_Problem-3\"><\/span>The Problem<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>IoT water monitoring generates vast data volumes\u2014thousands of readings per minute across dozens of parameters. Traditional systems lack the capability to transform this raw data into actionable intelligence.<\/p>\n<p>According to <strong>Gartner 2025 Data Analytics Survey<\/strong>, water utilities report that <strong>76%<\/strong> of sensor data collected is never analyzed. Organizations have more data than ever but struggle to extract value.<\/p>\n<h3 id=\"the-impact-on-iot-systems_2\"><span class=\"ez-toc-section\" id=\"The_Impact_on_IoT_Systems-3\"><\/span>The Impact on IoT Systems<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Without effective data management:<br \/>\n&#8211; Operators become overwhelmed by data volume<br \/>\n&#8211; Important events go undetected in noise<br \/>\n&#8211; Historical patterns remain undiscovered<br \/>\n&#8211; System optimization recommendations cannot be generated<\/p>\n<h3 id=\"proven-solutions_2\"><span class=\"ez-toc-section\" id=\"Proven_Solutions-3\"><\/span>Proven Solutions<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>Solution 3.1: Hierarchical Alert Architecture<\/strong><\/p>\n<p>Implement multi-level alerting systems:<\/p>\n<table>\n<thead>\n<tr>\n<th>Level<\/th>\n<th>Trigger<\/th>\n<th>Response<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Level 1<\/td>\n<td>Single parameter excursion<\/td>\n<td>Log and trend monitoring<\/td>\n<\/tr>\n<tr>\n<td>Level 2<\/td>\n<td>Sustained excursion or multi-parameter<\/td>\n<td>Operator notification<\/td>\n<\/tr>\n<tr>\n<td>Level 3<\/td>\n<td>Critical threshold or pattern match<\/td>\n<td>Immediate alert + recommended action<\/td>\n<\/tr>\n<tr>\n<td>Level 4<\/td>\n<td>Predicted failure or contamination<\/td>\n<td>Emergency response activation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>This structure reduces false positive rates by <strong>73%<\/strong> while ensuring critical events receive appropriate response.<\/p>\n<p><strong>Solution 3.2: Machine Learning Analytics<\/strong><\/p>\n<p>Deploy AI systems specifically designed for water quality:<br \/>\n&#8211; <strong>Anomaly detection<\/strong> identifies unusual patterns without predefined thresholds<br \/>\n&#8211; <strong>Predictive modeling<\/strong> forecasts future conditions<br \/>\n&#8211; <strong>Root cause analysis<\/strong> diagnoses underlying causes of problems<br \/>\n&#8211; <strong>Optimization recommendations<\/strong> suggest operational improvements<\/p>\n<p>Modern ML platforms achieve <strong>94%<\/strong> accuracy in identifying true water quality events while reducing false positives to <strong>&lt;5%<\/strong>.<\/p>\n<p><strong>Solution 3.3: Integrated Dashboard Visualization<\/strong><\/p>\n<p>Create intuitive operator interfaces that:<br \/>\n&#8211; Display summary metrics and trends at facility level<br \/>\n&#8211; Enable drill-down to specific sensors and time periods<br \/>\n&#8211; Highlight exceptions and recommended actions<br \/>\n&#8211; Provide historical context for current conditions<\/p>\n<p>Effective visualization reduces operator response time to alerts by <strong>65%<\/strong>.<\/p>\n<h2 id=\"implementation-roadmap\"><span class=\"ez-toc-section\" id=\"Implementation_Roadmap\"><\/span>Implementation Roadmap<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"phase-1-foundation-months-1-3\"><span class=\"ez-toc-section\" id=\"Phase_1_Foundation_Months_1-3\"><\/span>Phase 1: Foundation (Months 1-3)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li>Audit existing sensor network and identify critical gaps<\/li>\n<li>Deploy high-quality inline sensors with self-cleaning capability<\/li>\n<li>Implement edge computing devices at remote sites<\/li>\n<li>Establish centralized data historian<\/li>\n<\/ol>\n<h3 id=\"phase-2-intelligence-months-4-6\"><span class=\"ez-toc-section\" id=\"Phase_2_Intelligence_Months_4-6\"><\/span>Phase 2: Intelligence (Months 4-6)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li>Configure multi-level alert system<\/li>\n<li>Deploy basic machine learning anomaly detection<\/li>\n<li>Develop integrated dashboard interfaces<\/li>\n<li>Train operators on new monitoring workflows<\/li>\n<\/ol>\n<h3 id=\"phase-3-optimization-months-7-12\"><span class=\"ez-toc-section\" id=\"Phase_3_Optimization_Months_7-12\"><\/span>Phase 3: Optimization (Months 7-12)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li>Implement predictive maintenance models<\/li>\n<li>Develop operational optimization recommendations<\/li>\n<li>Integrate with SCADA and control systems<\/li>\n<li>Establish continuous improvement processes<\/li>\n<\/ol>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>IoT water quality monitoring challenges are significant but solvable. Success requires:<\/p>\n<ul>\n<li><strong>Reliable sensors<\/strong> with drift compensation<\/li>\n<li><strong>Robust connectivity<\/strong> with redundancy<\/li>\n<li><strong>Intelligent analytics<\/strong> that transform data to insight<\/li>\n<\/ul>\n<p>Organizations that address these challenges effectively achieve <strong>35%<\/strong> improvement in water quality compliance, <strong>28%<\/strong> reduction in operational costs, and <strong>52%<\/strong> faster response to water quality events.<\/p>\n<p>The technology exists. The solutions are proven. The question is whether your organization will capture the competitive advantage that IoT water monitoring provides.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>3 Critical Challenges in IoT Water Quality Monitoring (And Proven Solutions) Key Takeaways: &#8211; 68% of IoT water monitoring projects face significant data quality issues &#8211; Sensor calibration drift causes $45,000 average annual cost in false readings &#8211; Network connectivity problems affect 42% of remote monitoring deployments &#8211; Solutions exist for every major IoT water&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false},"categories":[1],"tags":[],"translation":{"provider":"WPGlobus","version":"2.12.0","language":"es","enabled_languages":["en","zh","es","de","fr","ru","pt","ar","ja","ko","it","id","hi","th","vi","tr"],"languages":{"en":{"title":true,"content":true,"excerpt":false},"zh":{"title":false,"content":false,"excerpt":false},"es":{"title":false,"content":false,"excerpt":false},"de":{"title":false,"content":false,"excerpt":false},"fr":{"title":false,"content":false,"excerpt":false},"ru":{"title":false,"content":false,"excerpt":false},"pt":{"title":false,"content":false,"excerpt":false},"ar":{"title":false,"content":false,"excerpt":false},"ja":{"title":false,"content":false,"excerpt":false},"ko":{"title":false,"content":false,"excerpt":false},"it":{"title":false,"content":false,"excerpt":false},"id":{"title":false,"content":false,"excerpt":false},"hi":{"title":false,"content":false,"excerpt":false},"th":{"title":false,"content":false,"excerpt":false},"vi":{"title":false,"content":false,"excerpt":false},"tr":{"title":false,"content":false,"excerpt":false}}},"_links":{"self":[{"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/posts\/30711"}],"collection":[{"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/comments?post=30711"}],"version-history":[{"count":0,"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/posts\/30711\/revisions"}],"wp:attachment":[{"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/media?parent=30711"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/categories?post=30711"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shchimay.com\/es\/wp-json\/wp\/v2\/tags?post=30711"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}