Our Children's Toxic Legacy

John Wargo

During this century, hundreds of billions of pounds of pesticides have been released to the global environment. How are we exposed to them? What can we do to protect ourselves? In this extraordinary analysis, John Wargo, one of the nation's leading experts in pesticide policy, traces the history of pesticide law and science, with a focus on the special hazards faced by children. Wargo presents a compelling case that children are more heavily exposed to some pesticides than adults and are especially vulnerable to some adverse effects. How should the fractured body of environmental law be repaired to manage the distribution of risk? This is the central question Wargo addresses as he suggests fundamental reforms of science and law necessary to understand and contain the health risks faced by children.

Politics & GovernmentPolitics & Government · medium confidencePolitics & Government81 signalsLlm Classifier: Politics & Government -> Politics & GovernmentOpen Library: Health risk assessment -> Medicine & Public HealthOpen Library: Health aspects -> Medicine & Public HealthScience & DiscoveryEnvironment, Conservation & PollutionMedicine, Health & the BodyPublic Health Systems
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

  2. 02 Central places

    None extracted with sufficient confidence.

  3. 03 Suggested argument

    Children receive heavier exposure to some pesticides than adults and are especially vulnerable to adverse effects, so environmental law and science must be fundamentally reformed to manage children’s health risks.

    Model confidence 66%

  4. 04 Reading orientation

    65 / 100 academic

    Academic

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 78%.