Industrial zones in Chile often face environmental crises affecting nearby community populations. There are numerous instances, yet solutions seen thus far are limited. The situation in Freirina, characterized by odors that led to the closure of a pig slaughterhouse, and the industrial area of Ventanas with its recurring impact on the Quintero and Puchuncaví locales, resulting in some operations' closure, can be cited as tangible examples of how the current solutions in place to manage environmental impact are insufficient.
Technological advancements like real-time data analysis have played a pivotal role in the decision-making process across a myriad of operational factors in various industries. Given the host of challenges companies operating complex production processes face, with a high potential to affect the environment, the companies must seek novel methods to supply timely information for decision-making in these impacted areas.
The Ventanas industrial area has been at the heart of the most significant environmental crises of the past decade in Chile. Localities such as Quintero, La Greda, and Puchuncaví have endured prolonged episodes of pollution, severely compromising the air quality for their residents.
Clearly, the current method for analyzing operational and atmospheric information is ill-suited for the region's needs. The strategy to prevent contaminative episodes evidently lacks accurate and timely information, with decisions being made post-crisis.
This is patently inadequate, stemming from industrial operations and authorities basing decisions on belated information. It's as if all decision-makers were retrospectively seeking information to guide their actions. Undoubtedly, reactive decisions come at a steep price: people's health and well-being!
The pressing question is: can we preempt these crises? The answer is a resounding yes. Data analytics, combined with digital solutions, can revolutionize our approach to environmental issues.
Data analytics isn't merely a tool for companies aiming to maximize profits; it's a potent ally in environmental preservation and combating pollution. Through sensors, monitoring stations, and other technologies, real-time data on emissions and atmospheric conditions can be gathered. By processing this information, digital solutions can forecast pollutant dispersion patterns, considering variables like wind speed, temperature, and humidity.
For instance, if an advanced analysis indicates that specific emissions, combined with certain atmospheric conditions, could lead to a high pollution episode within 48 hours, preventive measures can be adopted. These might include temporarily scaling back specific industrial operations, early warnings to communities, or initiatives to enhance pollutant dispersion.
The strength of data analytics lies in its ability to provide this predictive insight, empowering authorities and companies to make decisions based on projections rather than merely on past occurrences.
Acting proactively means deciding before the problem arises. In an environmental context, this means not waiting for pollution to reach hazardous levels before acting. With sophisticated digital tools, decisions can be grounded in predictive models that account for a range of variables like atmospheric conditions, wind and climatic patterns, emission levels, and operational efficiency metrics to avert heightened emissions.
In essence, data analytics offers timely information for anticipatory decision-making, minimizing health risks, facilitating industrial operations, enhancing efficiency levels, and fostering environmental responsibility by promoting emission reductions.
Inhabited Industrial zones in Chile, such as Ventanas, Calama, Tierra Amarilla, and many others, don't necessitate the closure of other industrial processes that contribute to employment and economic and social development. What's needed is a novel operational approach, leveraging available technology, accessing timely information, and employing digital systems that favor data analytics. This can alter the narrative concerning the relationship between production processes and the ecosystems they coexist with.
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