Introduction

This project is a central digital tool designed to digitize and automate the entire environmental monitoring process. The application was engineered to seamlessly connect the steps from sample collection in the field to lab analysis and final report generation. The main goal was to increase efficiency, minimize errors, and ensure the transparency and reliability of environmental data.

Previously, environmental monitoring faced many challenges due to inconsistent processes and manual errors. This application was created to thoroughly solve these issues by providing a comprehensive technological solution that ensures data consistency and quality.

Team size

4 members

Industry

Environmental

Technology

Serverless Framework, Python, AWS, React, React Native

Highlights

 Value Delivered

  • Increased Efficiency and Reduced Errors: The digitized and automated process saved considerable time and eliminated errors from manual data entry.
  • Ensured Data Transparency and Reliability: The duplicate sample mechanism is a powerful tool for cross-checking and validating lab results, ensuring data is always accurate and reliable.
  • Seamless Workflow: Connecting all stages from the field to analysis and reporting created a smooth and efficient workflow.
  • Advanced Technological Solution: Using modern technologies like Serverless and React built a sustainable, easy-to-maintain, and scalable system for the future.

Challenges

  • Manual and Inconsistent Processes: Field data collection and processing were unsynchronized, leading to wasted time and a high potential for errors.
  • Lack of Lab Data Quality Control: It was difficult to verify the accuracy of lab analysis results, creating a risk of unreliable data.
  • Scattered and Opaque Data: Information from the field, lab, and reports was not seamlessly connected, making management and retrieval challenging.

Solutions

  • Mobile App for Automated Sample Collection: The application was developed using React Native to digitize and automate sample collection in the field, making it easy for technicians to record information.
  • Duplicate Sample Mechanism for Quality Assurance: This is a core and unique feature of the project. Technicians submit two identical samples under two different names. The system automatically cross-checks them: if the lab analysis results for the two samples differ, the system flags a procedural error in the lab. This helps managers quickly detect and fix mistakes, ensuring absolute data reliability.
  • End-to-End Workflow Automation: The entire process, from sample collection to lab submission, analysis, and reporting, is automated.
  • Robust and Flexible Back-end System: We used a Serverless Framework with Python on AWS. This architecture ensures the system is efficient, easily scalable, and cost-effective.
  • Modern User Interface: The web interface was developed with React, providing a smooth and intuitive user experience.