AQMS

In response to growing concerns about air quality, Lucrum ERP offers an Internet of Things (IoT) solution – the Air Quality Monitoring System (AQMS). This system provides real-time data on various pollutants present in the surrounding environment. By deploying strategically placed sensors, AQMS gathers crucial information on factors like particulate matter, ozone, and carbon monoxide levels. Lucrum ERP's AQMS seamlessly integrates with your existing data infrastructure, enabling you to monitor air quality trends, identify potential issues, and make informed decisions to promote a healthier environment.

Citizen Science Integration

This sub-module allows for crowdsourced data collection through integration with mobile apps or wearable sensors carried by citizens. This can provide a wider spatial coverage and real-time air quality updates from user locations.

Key Features

  • Mobile App Integration: Connects seamlessly with user-friendly mobile apps for data collection and also enables citizens to contribute real-time air quality data from their locations.
  • Wearable Sensor Integration: Integrates with wearable air quality sensors for personal exposure monitoring; additionally, provides valuable insights into individual air quality experiences.
  • Data Aggregation & Visualization: Aggregates crowdsourced data alongside fixed sensor readings. Furthermore, it presents comprehensive air quality maps and visualizations for improved awareness.

Health Impact Prediction

This sub-module integrates with health data sources and weather forecasts to predict potential health risks based on air quality readings and weather patterns. It can issue targeted advisories for vulnerable populations.

Key Features

  • Health Data Integration: Connects with health databases (e.g., respiratory illness records) for trend analysis and identifies correlations between air quality and health outcomes.
  • Weather Pattern Analysis: Factors in weather data like temperature and humidity for a holistic view.
  • Predicts how weather conditions might influence health impacts of air pollution.
  • Targeted Advisories: Generates risk assessments for vulnerable populations (e.g., asthmatics, children), also issues targeted advisories through mobile apps or local channels.

Environmental Source Attribution

This sub-module utilizes data analysis tools and historical data to identify potential sources of pollution based on wind patterns and sensor readings, aiding targeted mitigation efforts.

Key Features

  • Advanced Data Analysis: This sub-module analyzes sensor readings, wind patterns, and historical data. It also employs machine learning algorithms for enhanced source identification.
  • Emission Source Mapping: Creates visual representations of potential pollution sources based on data analysis. Moreover, it assists in pinpointing industrial facilities or geographical areas contributing to air quality issues.

Smart Building Integration

This sub-module allows AQMS to connect with smart building systems, automatically triggering actions like increased ventilation or air filtration systems when air quality thresholds are crossed.

Key Features

  • Real-Time Data Exchange: Transfers air quality data from AQMS sensors to smart building systems. Provides continuous insights for automated responses.
  • Automated Action Triggers: This particular sub-module helps activating pre-defined actions based on air quality thresholds.
  • Improved Efficiency: Optimizes energy consumption by adjusting ventilation based on real-time needs and reduces reliance on manual intervention.

Air Quality Forecasting

This sub-module leverages machine learning to analyze historical data, weather patterns, and current readings to predict future air quality trends, enabling proactive measures.

Key Features

  • Machine Learning Algorithms: Integrates with machine learning models trained on historical air quality data, weather patterns, and sensor readings and analyzes complex relationships to predict future pollutant levels.
  • Forecasting Capabilities: Helps generate short-term and long-term air quality forecasts for informed planning and mitigation strategies. Furthermore, it enables proactive measures to reduce exposure risks or optimize resource allocation.