Environmental Science: Systems, Evidence, and Choices

This unit is the quantitative backbone of the whole year. Your child learns that energy moves through a food web in one direction and mostly dissipates as heat at each step (roughly 90% lost per level), while matter cycles endlessly through living things, rock, water, and air. They move from naming things (producer, consumer, decomposer) to actually measuring them, estimating real biomass in a plot outside and predicting energy available higher up the food chain.

1. The 10% trophic energy-transfer rule+

   Given a food chain with population counts or biomass at each level, calculate the energy or biomass available at the next trophic level using the 10% transfer rule.

2. The mechanism of energy loss between trophic levels+

   Explain why approximately 90% of energy is lost, rather than passed on, at each trophic transfer, citing metabolism, movement, and heat loss as mechanisms.

3. The distinction between biomass pyramids and energy pyramids+

   Differentiate biomass pyramids from energy pyramids, identifying the specific ecosystem condition (e.g., aquatic systems with fast-turnover producers) under which a biomass pyramid can appear inverted while the energy pyramid does not.

4. Biomass estimation from field-plot sampling data+

   Given real or simulated plot-sampling data (plant counts, dry mass estimates by species/area), estimate total producer biomass per square meter and identify sources of sampling error.

5. The carbon or nitrogen biogeochemical cycle and its rate-limiting processes+

   Construct a labeled diagram of the carbon or nitrogen cycle that connects at least four reservoirs and processes and explains one bottleneck (a process that limits flow rate) as a causal constraint on the whole cycle, not just a labeled stage.

6. The relationship between GPP, NPP, and respiration+

   Compare gross primary productivity (GPP) and net primary productivity (NPP) for two ecosystems with different respiration rates, and infer which ecosystem has more energy available to higher trophic levels.

7. Transfer of trophic energy-transfer and biogeochemical cycling constraints to an unfamiliar engineered or natural system+

   Given a novel biome or engineered system never discussed in class (e.g., an aquaponics tank or a Martian greenhouse habitat design), predict where the energy-transfer and matter-cycling constraints taught in this unit would create the tightest bottleneck, and justify the prediction using the 10% rule and cycle mechanisms.

8. The internal consistency of a food-web capacity claim against energy-transfer constraints+

   Critique a claim such as 'a food web can support one more predator species with no other change' by evaluating whether the claim is consistent with the 10% rule and available biomass data.

This unit takes Unit 1's idea that ecosystems have real limits and turns it into math that predicts when a population will outgrow what its system can support. Then it complicates that picture: your child learns that a bigger population doesn't automatically mean bigger environmental impact, consumption per person matters just as much, sometimes more.

1. Exponential vs. logistic population growth patterns in real time-series data+

   Given a dataset of population counts over time for an unfamiliar species, determine whether the data follow an exponential or logistic pattern and identify the specific data point where growth rate changes, if any.

2. The exponential growth equation+

   Execute the exponential growth calculation (N_t = N_0 * e^(rt)) to predict a future population size given an initial population, growth rate, and time interval.

3. The mechanism linking population density to slowing growth rate (density-dependent limiting factors)+

   Explain why a population's growth rate slows as it approaches carrying capacity, using the concept of density-dependent limiting factors.

4. The relationship between population size and per-capita consumption as joint contributors to total impact+

   Given population and per-capita consumption data for two contrasting regions (one high-population/low-consumption, one lower-population/high-consumption), compare their total environmental impact and identify which variable, population or affluence/technology, is doing more work in each case.

5. The IPAT identity as a multiplicative model of environmental impact+

   Implement the IPAT identity (Impact = Population x Affluence x Technology) to calculate and compare total environmental impact across two or more regions or scenarios with different population, affluence, and technology values.

6. The information-loss inherent in single-number sustainability metrics (ecological footprint as example)+

   Critique a published claim that cites ecological footprint or a similar single-number sustainability metric, identifying at least two categories of environmental impact the metric does not capture.

7. Carrying capacity and multiplicative-driver (IPAT-structure) reasoning applied to a non-population resource system+

   Given a completely unfamiliar resource-consuming system outside of population ecology (e.g., a groundwater aquifer under municipal withdrawal, or a fishery under harvest quotas), apply the logistic growth/carrying-capacity model and the IPAT-style decomposition to argue whether the system is approaching, at, or in overshoot of its capacity, and whether the larger driver is extraction rate or number of users.

8. Integration of growth-curve model, limiting-factor identification, and IPAT-based driver analysis into a single evidence-based argument+

   Given a real regional dataset (the summative task), construct a written argument integrating a fitted growth curve, an identified limiting factor, and an IPAT-based claim about whether population or consumption is the larger driver of impact in that specific region.

This unit explains the greenhouse effect as an actual physical mechanism, specific gas molecules absorbing and re-emitting infrared radiation, instead of the vague 'trapping heat like a blanket' version most people carry around. Then it uses that mechanism to make sense of two real datasets: the Keeling curve (CO2 rising over decades) and ice-core records going back 800,000 years.

1. The electromagnetic spectrum bands for incoming shortwave and outgoing longwave radiation+

   Given a diagram of the electromagnetic spectrum labeled with wavelength bands, identify which band corresponds to incoming solar radiation and which corresponds to outgoing radiation from Earth's surface.

2. Selective molecular absorption of infrared radiation by greenhouse gases versus transparency of N2/O2+

   Explain why CO2, CH4, and H2O vapor absorb outgoing infrared radiation while N2 and O2 do not, using molecular structure as the mechanism.

3. The gap between a no-atmosphere radiative balance prediction and Earth's observed surface temperature+

   Using the contrasting cases of a bare-rock planet with no atmosphere versus Earth's actual measured surface temperature, infer that an additional energy-retaining mechanism must be operating on Earth.

4. The Keeling curve's seasonal cycle versus its long-term trend+

   Read the Keeling curve to distinguish the annual seasonal oscillation (biological carbon uptake/release) from the multi-decade upward trend (net atmospheric accumulation).

5. The ice-core CO2/temperature record compared against the Keeling curve's post-industrial segment+

   Compare the ice-core CO2/temperature record with the Keeling curve to determine which time periods in the combined record show natural variation and which show a post-industrial pattern inconsistent with the prior 800,000-year range.

6. Positive and negative climate feedback loops (ice-albedo, water vapor, cloud) and their boundary conditions+

   Given a description of a specific feedback process (ice-albedo, water-vapor, or cloud feedback), classify it as amplifying (positive) or dampening (negative) the initial warming and state the boundary condition under which the loop changes or stops.

7. The causal chain from fossil fuel combustion through radiative forcing to measured warming+

   Construct a mechanistic causal chain connecting fossil fuel combustion, atmospheric CO2 concentration, radiative forcing, and measured global temperature, explicitly stating what physical relationship connects each pair of steps.

8. Falsifiability criteria applied to the CO2-radiative-forcing-warming mechanism+

   State a specific, measurable observation in atmospheric or paleoclimate data that would falsify the claim that rising CO2 causes the observed warming, and explain why that observation would contradict the mechanism rather than merely fail to support it.

9. The greenhouse mechanism applied to a hypothetical planetary atmosphere with different gas composition+

   Given an unfamiliar planetary scenario (e.g., a planet with a much thicker or thinner CO2 atmosphere than Earth, or one with no water vapor feedback), predict the qualitative direction and reason about the relative magnitude of its greenhouse effect compared to Earth's.

10. The distinction between mechanism-level scientific claims and magnitude/policy-level claims about climate change+

    Distinguish a scientific claim about mechanism ('CO2 causes warming because it absorbs and re-emits infrared radiation') from a claim about magnitude or policy ('we must cut emissions by X% by Y date'), recognizing that disputing the second does not require disputing the first.

This unit is about method, not new climate facts. It teaches the actual four-part test scientists use before saying 'this specific event was caused by human-driven warming': a stated mechanism, confounders considered and ruled out, at least two independent lines of evidence, and a confidence level that matches how specific the claim is.

1. The greenhouse effect / radiative forcing mechanism+

   State the physical mechanism of the greenhouse effect (radiative forcing) taught in Unit 3, without re-derivation.

2. The distinction between weather and climate as timescale and statistical concepts+

   Differentiate a weather observation from a climate observation given a described event (e.g., 'record cold week in Chicago' vs. '30-year warming trend in Chicago').

3. Proxy data as indirect evidence of past climate (ice cores, tree rings, coral records)+

   Explain how a proxy dataset (ice core, tree ring, or coral record) provides indirect evidence of past temperature, identifying the physical or biological relationship the proxy depends on.

4. Climate model validation via hindcasting against historical data+

   Evaluate whether a climate model has been adequately validated by comparing its hindcast output to the historical instrument record and calculating the size of the residual.

5. The distinction between a defensible causal attribution and an unsupported correlation, in climate contexts+

   Given two attribution claims about the same general phenomenon (one well-supported by multiple independent lines of evidence, one supported by a single correlation), identify what specific additional evidence separates the defensible claim from the merely plausible one.

6. A complete, structured attribution argument for a specific observed event+

   Construct a complete attribution argument for a specific observed climate-related event, stating the proposed mechanism, at least one confounder considered and how it was ruled out, and at least two independent lines of evidence.

7. IPCC-style confidence/likelihood language applied to a specific claim versus a general phenomenon+

   Assign and justify an appropriate confidence level (using IPCC-style likelihood language) to a specific attribution claim, distinguishing the confidence appropriate to the general phenomenon from the confidence appropriate to a specific regional or single-event claim.

8. Attribution reasoning applied to a genuinely novel, unrehearsed real-world claim+

   Given a contested real-world attribution claim never discussed in class (e.g., a specific claim about a named recent extreme-weather event not covered in the unit), write an evidence-based argument rating confidence and citing at least two independent data sources, applying the full attribution framework without a provided graphic organizer.

9. The structural completeness of an attribution argument as a criterion for evaluating its defensibility+

   Critique a peer's or a published source's attribution argument by identifying which required structural element (mechanism, confounder ruled out, independent evidence, or proportionate confidence level) is missing or weak, and explain why its absence weakens the argument's defensibility.

This unit looks at how ecosystems absorb damage up to a point, and what happens past that point, one species disappearing can cascade through the energy pathways from Unit 1. Your child uses Unit 4's attribution method to figure out, given a real local decline, which of several competing stressors (habitat loss, climate, invasive species) actually explains it.

1. Trophic cascades following keystone species removal+

   Students trace the direct sequence of population changes across trophic levels that follows the removal of a keystone predator from a given food web diagram.

2. Structural basis of keystone status (connectivity and functional uniqueness)+

   Students explain why removing a keystone species produces disproportionate ecosystem change while removing a redundant mid-level species does not, citing the structural difference between the two cases.

3. Resilience thresholds and tipping points in ecosystem stress-response graphs+

   Given a graph of an ecological indicator against increasing stress with a nonlinear, delayed decline, students identify the region representing an undetected approach to a threshold and state what an early-warning indicator would need to show.

4. Habitat fragmentation vs. climate stress vs. invasive species as competing stressor explanations+

   Students distinguish habitat fragmentation, climate stress, and invasive species as competing candidate explanations for a described population decline, identifying which observed evidence is consistent with each.

5. Quadrat sampling and point-count field survey procedures+

   Students collect a species-count dataset from a local site using quadrat sampling or point-count method, correctly following the standardized procedure.

6. Attribution of a specific biodiversity change to one of several competing stressors+

   Students construct a written argument identifying which of 2-3 candidate stressors best explains an observed local species decline, naming a mechanism and explicitly ruling out the less-supported alternatives using timing and evidence.

7. Comparison of local survey data against regional historical baseline data+

   Students compare their local species-count data to regional historical baseline data to determine whether an observed count constitutes a meaningful decline, an increase, or is within normal fluctuation.

8. Applying trophic cascade and threshold concepts to evaluate an untaught conservation intervention+

   Students evaluate whether a novel proposed conservation intervention (not discussed in class) would plausibly restore resilience to a described degraded ecosystem, given its cascade structure and threshold position.

9. Background extinction rate as a comparison baseline for current extinction rates+

   Students recall the definition of background extinction rate and state how current estimated extinction rates compare to it in order of magnitude.

This unit treats pollution as a disruption of the water and nitrogen cycles from Unit 1, pollutants travel along traceable paths, not appear from nowhere. Your child learns to trace contamination using a source-pathway-receptor model, understands eutrophication (nutrient overload in water) as a threshold problem with a delay before visible symptoms, and runs a real water-quality test on a local water source.

1. The nitrogen cycle stages (fixation, nitrification, runoff, denitrification) as they appear in a watershed diagram+

   Given a watershed diagram with a labeled fertilizer application area, storm drain, and downstream pond, students correctly identify which cycle process (fixation, nitrification, runoff, denitrification) each stage represents, matching the labels used in the Unit 1 nitrogen cycle diagram.

2. Point source vs. nonpoint source pollution classification criteria+

   Students classify a set of 8-10 real-world pollution scenarios (a factory discharge pipe, agricultural runoff from multiple farms, a leaking septic tank, urban street runoff, a wastewater treatment outfall) as point source or nonpoint source, justifying each classification by whether a single identifiable discharge location exists.

3. The source-pathway-receptor model applied to an untaught contamination scenario+

   Given a new, previously unseen contamination scenario (e.g., a chemical spill upstream of a municipal water intake), students construct a complete source-pathway-receptor chain, explaining the mechanism by which the pollutant travels from source to receptor and citing what would need to be true for that pathway to actually connect them.

4. The lag between rising nutrient loading and the visible onset of eutrophication as a threshold phenomenon+

   Students explain why a lake showing no visible algal bloom in year 1 of a nutrient-loading dataset can still cross a eutrophication threshold by year 4, using the concept of a limiting nutrient and cumulative loading, without relying on visible symptoms as the only evidence of change.

5. The distinction between bioaccumulation (within-organism, over time) and biomagnification (across trophic levels, at one time)+

   Given paired data on a single fish's tissue mercury concentration over its lifetime and a four-level food chain's mercury concentration at one point in time, students differentiate which case is bioaccumulation and which is biomagnification, identifying which axis (time vs. trophic position) distinguishes them.

6. Dose-response thresholds as receptor-dependent reference values, not intrinsic pass/fail properties of a number+

   Students interpret a single numeric water-quality result (e.g., nitrate at 8 mg/L) against two different stated reference thresholds (EPA drinking water MCL and a documented ecological eutrophication threshold) and determine which threshold is relevant given a stated receptor, explaining why the same number yields different verdicts.

7. PH, turbidity, and nitrate test-strip procedure and reading technique+

   Students correctly execute pH, turbidity, and nitrate test-strip procedures on a water sample, recording results with correct units and comparing their technique against the manufacturer's reference color chart.

8. An original source-pathway-receptor causal argument and remediation tradeoff for a self-collected local water sample+

   Using their own collected water-quality data and a locally plausible geographic context, students construct and justify a complete, connected source-pathway-receptor argument for any anomalous reading, and propose one remediation action along with at least one cost it would impose and on whom.

9. The structural difference between a connected causal chain and a list of disconnected facts in a source-pathway-receptor argument+

   Students critique a peer's draft source-pathway-receptor chain, identifying whether each link is a genuine causal connection or a restated fact, and write one specific revision suggestion referencing the missing or weak link.

This unit takes the source-pathway-receptor model from Unit 6 and moves it into the air, where wind and temperature layers (inversions) change who actually breathes a pollutant, and where individual differences (age, indoor time) change how much harm the same dose does. It ends with a comparative case using real public air-sensor data for two nearby locations.

1. Primary vs. secondary air pollutants (e.g., soot vs. ground-level ozone)+

   Classify a given air pollutant scenario as primary or secondary based on whether it is emitted directly or formed through atmospheric reaction.

2. Inversion layers and atmospheric transport of pollutants+

   Explain how an inversion layer changes pollutant concentration at ground level compared to normal atmospheric mixing conditions.

3. Dose-response curves; threshold vs. no-threshold models+

   Interpret a dose-response curve to determine whether it better fits a threshold or a no-threshold (linear) model for a given pollutant-outcome pair.

4. Comparative shape of dose-response curves across pollutants+

   Compare two dose-response curves for different pollutants to explain why the same numeric exposure level can carry different levels of risk.

5. Source-pathway-receptor model applied to a new air-quality dataset+

   Construct a causal chain (source to pathway to receptor) for a real air pollutant using public sensor data for a specific, previously unexamined location.

6. Causal sufficiency of dose-response evidence for a specific health claim+

   Evaluate whether a stated health-outcome claim about a pollutant is supported by the available dose-response evidence or relies on correlation alone.

7. Empirical distribution of exposure across demographic groups (environmental justice as a data question)+

   Analyze public sensor and demographic data for two locations to identify whether exposure to a pollutant is distributed disproportionately across groups.

8. Integrated causal argument linking pathway, dose-response, and distributional exposure for a novel location pair+

   Generate a written case brief that connects pollutant transport pathway, dose-response evidence, and disproportionate exposure into a single causal argument for two real, previously unexamined locations.

This closing unit asks your child to combine four things they've built all year, energy-return math from Unit 1, carbon intensity and confidence levels from Units 3/4, demand forecasting from Unit 2, and who-bears-the-cost thinking from Units 6/7, into one real project: comparing three or more actual energy sources for a real local or regional decision. Almost nothing here is brand-new content; it's mostly research, drafting, peer review, and a final presentation.

1. Energy return on energy invested (EROEI) as a ratio of output energy to invested energy+

   Given a fuel source's energy output and the energy cost of extracting/processing/delivering it, calculate its EROEI and correctly identify which value belongs in the numerator versus denominator.

2. Conversion from nameplate capacity to expected output using capacity factor+

   Given a fuel source's nameplate capacity and its published capacity factor, calculate expected annual energy output and identify the value's units correctly (MWh, not MW).

3. The non-dominance of any single metric (EROEI, carbon intensity, LCOE) across competing energy sources+

   Compare three energy sources' EROEI, carbon intensity, and LCOE data presented side by side and explain why ranking by one metric alone produces a different 'best' choice than ranking by another.

4. The distinction between evidence claims and value judgments in energy-policy argument+

   Classify a given statement about an energy tradeoff as an evidence claim (checkable against a data source) or a value judgment (a stated priority), applying a consistent test across at least six varied example statements.

5. Demand forecasting applied to grid capacity planning+

   Given demand-growth data for a hypothetical or real region, forecast future energy demand using exponential or logistic growth math and identify which existing energy mix would fail to meet that forecast without additional capacity.

6. An original multi-source energy tradeoff analysis for a self-selected real scenario+

   Select a real local or regional energy-decision scenario and construct an original comparative tradeoff analysis integrating EROEI, carbon intensity, and distributed cost-benefit data across at least three energy sources not covered in the class's worked examples.

7. Diagnosing the source of disagreement between competing energy-policy recommendations+

   Given a completed case study (not their own), identify whether a stated disagreement between two possible recommendations stems from a factual/data dispute or a difference in value-weighting, and justify the distinction using specific text from the case study.

8. The physical constraint linking energy inputs, conversion losses, and deliverable output in an energy system+

   Explain, using a specific energy source as the case, why a system's measurable output cannot exceed what its energy inputs and internal conversion losses allow.

9. The definition and units of carbon intensity+

   Recall the definition and units of carbon intensity (grams CO2-equivalent per kWh) without reference support.

10. Oral presentation of an original energy tradeoff analysis with explicit evidence/value separation+

    Deliver a case-study presentation that explicitly separates evidence claims from value judgments and states a reasoned recommendation, responding to at least one clarifying question from the audience about a specific data source used.